I was bouncing between ChatGPT Pro, Claude web, and Cursor like a pinball with a deadline. Copy from o1 pro. Paste into my editor. Fix the bug it introduced. Pray it works. Try Cursor for a second opinion. Watch it rewrite my entire file when I asked for one measly line.
Rinse. Repeat. Question your life choices.
(We’ve all been there. And if you say you haven’t, well, I’m not sure I believe you.)
Then May hit. Anthropic added Claude Code to their Max plan—same $200/month I was already burning on ChatGPT Pro, but now I could stop copy-pasting and start orchestrating.
That shift changed everything.
Here’s the thing: I wrote 30+ articles this year documenting every breakthrough, every spectacular failure, every “wait, that’s how it’s supposed to work?” moment. If you only read one piece from me in 2025—make it this one.
What follows are the 4 immutable laws of Vibe Coding I discovered this year. They turned chaotic AI sessions into systematic, predictable wins. Once you see them, you can’t unsee them.
Ready? Let’s go.
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Rule #1: The Blueprint Is More Important Than The Code
Let me tell you about the single biggest mistake I see developers make.
They type “build me a task management app” and hit Enter. Claude generates code. Components. Database schemas. Authentication logic.
And then… it’s nothing like what they imagined.
They blame the AI. “It hallucinated again.”
But here’s what I’ve learned after shipping dozens of projects with Claude Code: hallucinations are usually just ambiguity in your prompt. That’s it. That’s the secret nobody wants to admit.
AI is a terrible architect. Give it vague instructions, and it fills in the blanks with whatever patterns it’s seen most often. (Which, spoiler alert, aren’t YOUR patterns.)
But AI is an amazing contractor.
Give it clear blueprints—specific requirements, explicit constraints, visual references—and it executes with surgical precision. Like a really talented carpenter who just needs you to stop saying “make it nice” and start handing over actual measurements.
The technique: Interview yourself first
Instead of asking Claude to “build me an app,” I use a brainstorming prompt (inspired by McKay Wrigley and Sabrina Ramonov) that flips the entire script.
The AI interviews me.
“What’s the core problem this solves?”
“Who uses it?”
“What does the main screen look like?”
“What happens when the user clicks X?”
By the time I’ve answered those questions, I’ve got a Product Requirements Document. Not AI-generated slop—my vision, clarified.
Claude becomes the junior dev who asks great questions before writing a single line of code. I stay the architect who actually understands what we’re building.
(This is the way it should be.)
The secret weapon: ASCII wireframes
Text descriptions get misinterpreted. Every. Single. Time.
You say “a sidebar with navigation.” Claude hears “full-width hamburger menu.”
So I started including ASCII art wireframes in my prompts:
Sounds primitive, right? Almost embarrassingly low-tech.
The results say otherwise.
When I started including visual plans, my first-try success rate hit 97%. Claude understood layout and hierarchy immediately. No more “that’s not what I meant” rewrites. No more three rounds of “closer, but still wrong.”
👉 The takeaway: Stop typing code and start drawing maps. The blueprint is where the real work happens.
Rule #2: Separate The “Thinker” From The “Builder”
At the beginning, I was using Claude Code for everything.
Planning. Building. Reviewing. Debugging.
One model to rule them all.
And it almost worked.
Almost.
But I kept running into the same problems. Claude would rewrite perfectly good code. Add complex abstractions I never asked for. Solve a simple bug by restructuring half my app.
I asked for email OTP login. I got a 12-file authentication framework.
I asked to fix a type error. Claude decided my entire architecture was wrong.
(It wasn’t. I promise you, it wasn’t.)
The discovery: Specialized roles
Then I stumbled onto a workflow that changed everything—and honestly, I felt a little silly for not seeing it sooner.
Use one model to think. Use another to build.
For me, that’s GPT-5/Codex (The Thinker) and Claude Code (The Builder).
Codex asks clarifying questions. It creates comprehensive plans. It reviews code like a senior engineer who’s seen every possible edge case and still remembers them all.
Claude Code executes. Fast. Reliably. It handles files, terminal commands, and edits without wandering off into philosophical debates about code architecture.
Together? Magic.
The review loop
The workflow looks like this:
Plan (Codex): Describe what I want to build. Codex asks questions, creates a detailed implementation plan.
Build (Claude Code): Feed the plan to Claude. Let it execute.
Review (Codex): Paste the implementation back to Codex. It checks against the original plan, catches bugs, finds edge cases.
That third step—the review loop—catches issues that single-model workflows miss every time. EVERY time.
Taming the overengineering monster
Claude has a tendency to overcomplicate. It’s well-documented at this point. (If you’ve used it for more than a week, you know exactly what I’m talking about.)
My fix? The Surgical Coding Prompt.
Instead of “add this feature,” I tell Claude:
“Analyze the existing patterns in this codebase. Implement this change using the minimal number of edits. Do not refactor unless explicitly asked. Show me the surgical changes—nothing more.”
From 15 files to 3 files. From 1000+ lines to 120 lines.
Same functionality. 90% less complexity.
👉 The takeaway: Treat your AI models like a team, not a swiss-army knife. Specialized roles produce specialized results.
“Why do I keep explaining the same patterns over and over?”
Every new project, I’d spell out my authentication approach. My database schema conventions. My error handling patterns. Every. Single. Time.
Claude would forget by the next session. Sometimes by the next prompt.
I was treating AI like a goldfish with a keyboard.
(No offense to goldfish. They’re trying their best.)
The “I know kung fu” moment
Then Claude launched Skills—and everything clicked.
Skills let you package your coding patterns into reusable modules. Instead of explaining “here’s how I do authentication” for the 47th time, you create an auth-skill. Enable it, and Claude instantly knows your entire implementation.
The exact patterns. The exact folder structure. The exact error messages.
Every project uses the same battle-tested approach. Zero drift. Zero “well, last time I used a different library.”
It’s like downloading knowledge directly into Claude’s brain.
Matrix-style. (Hence the name.)
Building your first skill
The process is stupidly simple:
Take code that already works in production
Document the patterns using GPT-5 (it’s better at documentation than execution)
Transform that documentation into a Claude Skill using the skill-creator tool
Deploy to any future project
The documentation step matters. GPT-5 creates clean, structured explanations of your existing implementations. Claude Skills uses those explanations to replicate them perfectly.
The compound learning effect
Here’s where it gets really interesting.
I built an Insights Logger skill that captures lessons while Claude “code”. Every architectural decision, every weird bug fix, every “oh that’s why it works that way” moment—automatically logged.
At the end of each session, I review those insights. The good ones get promoted to my CLAUDE.md file—the permanent knowledge base Claude reads at the start of every project.
Each coding session builds on the last. Compound learning, automated.
👉 The takeaway: Prompting is temporary. Skills are permanent. If you’re explaining something twice, you’re doing it wrong.
Rule #4: Friction Is The Enemy (So Automate It Away)
Let me describe a scene you’ll recognize.
You’re deep in flow state. Claude Code is humming along. Building components, wiring up APIs, making real progress.
And then:
Allow Claude to run `npm install`? [y/n]
You press Enter.
Allow Claude to run `git status`? [y/n]
Enter.
Allow Claude to run `ls src/`? [y/n]
Enter. Enter. Enter. Enter. Enter.
By prompt #47, you’re not reading anymore. You’re a very tired seal at a circus act nobody asked for.
(Stay with me on this metaphor—it’s going somewhere.)
Anthropic calls this approval fatigue. Their testing showed developers hit it within the first hour of use.
And here’s the terrifying part: the safety mechanism designed to protect you actually makes you less safe. You start approving everything blindly. Including the stuff you should actually read.
The sandbox solution
Claude Code’s sandbox flips the entire model.
Instead of asking permission for every tiny action, the sandbox draws clear boundaries upfront. Work freely inside them. Get blocked immediately outside them.
On Linux, it uses Bubblewrap—the same tech powering Flatpak. On macOS, it’s Seatbelt—the same tech restricting iOS apps.
These boundaries are OS-enforced. Prompt injection can’t bypass them.
Claude can only read/write inside your project directory. Your SSH keys, AWS credentials, shell config? Invisible. Network traffic routes through a proxy allowing only approved domains.
You run /sandbox, enable auto-allow mode, and suddenly every sandboxed command executes automatically. No prompts. No friction. No approval fatigue.
The 84% reduction in permission prompts? Nice. The kernel-level protection that actually works? Essential.
Parallel experimentation with Git Worktrees
Here’s another friction point that kills vibe coding: fear of breaking the main branch.
My fix: Git Worktrees with full isolation.
Standard worktrees share your database. They share your ports. Three AI agents working on three features leads to chaos. (Ask me how I know.)
I built a tool that gives each worktree its own universe. Own working directory. Own PostgreSQL database clone. Own port assignment. Own .env configuration. Now I run three experimental branches simultaneously. Let three Claude instances explore three different approaches. Pick the winner. Delete the losers.
No conflicts. No fear. No “let me save my work before trying this crazy idea.”
👉 The takeaway: Safe environments allow for dangerous speed. Eliminate friction, and experimentation becomes free.
Ready to set it up?
Claude Code Sandbox Explained walks through the complete configuration—including battle-tested configs for Next.js, WordPress, and maximum paranoia mode.
The Synthesis: What Separates Hobbyists From Shippers
These 4 rules are what separate “people who play with AI” from “people who ship software with AI.”
Rule #1: The blueprint is more important than the code.
Rule #2: Separate the thinker from the builder.
Rule #3: Don’t just prompt—teach skills.
Rule #4: Friction is the enemy.
Each rule builds on the last.
Clear blueprints feed into specialized models. Specialized models benefit from reusable skills. Reusable skills only matter if friction doesn’t kill your flow.
It’s a system. Not a collection of random tips.
Where to start
Don’t try to implement all four at once.
That’s a recipe for burnout.
Start with Rule #4. Enable the sandbox. Regain your sanity. Stop being a tired circus seal.
Then move to Rule #1. Before your next feature, write the PRD first. Interview yourself. Draw the ASCII wireframe.
Rule #2 and Rule #3 come naturally after that. You’ll feel the pain of overengineering (and want specialized roles). You’ll get tired of repeating yourself (and want skills).
The system reveals itself when you need it.
Your challenge for 2026
Pick one project you’ve been putting off. Something that felt too complex for AI assistance.
Apply Rule #1: Write the blueprint first. ASCII wireframes and all.
Apply Rule #4: Set up the sandbox before you start.
Then let Claude execute.
Watch what happens when AI has clear boundaries and clear instructions. Watch how different it feels when you’re orchestrating instead of babysitting.
What will you build first?
Here’s to an even faster 2026.
Now go ship something.
This post synthesizes a year’s worth of vibe coding experimentation. Browse the full archive to dive deeper into any technique—from CLAUDE.md setup to sub-agent patterns to WordPress automation.
Quick note: If you’re expecting Claude Skills Part 3 this week – it’s coming next week! I’m presenting this WordPress automation topic at WordCamp Taiwan 2025, so I’m switching up the schedule. Part 3 of the Claude Skills series (the one you’ve been waiting for drops next week. Trust me, it’ll be worth the wait.
It’s Friday night, 11:40 p.m.
You’re staring at wp-admin on client site number nine.
Updates → Clear cache → Regenerate CSS → Woo settings check → Lighthouse quick run → Screenshot the results.
You could do it with your eyes closed.
And that’s the problem.
You’re not developing anymore.
You’re a highly-paid mouse with a checklist.
More than 80% of your workload every week is just doing the same 20-30 clicks over and over and over.
What if I told you an AI agent could do all these clicks for you?
Not through APIs. Not through custom code.
Through the exact same wp-admin interface you’re clicking right now.
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The WordPress Pain Nobody Talks About
Here’s what your week actually looks like:
Monday morning: Update plugins on 5 client sites (45 minutes of clicking)
Wednesday: Deploy the same security settings to 8 new sites (90 minutes)
Thursday: Set up another identical WooCommerce store (3 hours)
Friday: Check all sites for updates, run reports (2 hours)
Sound familiar?
You didn’t become a WordPress developer to be a professional button-clicker.
Yet here you are, executing the same sequences like a well-trained automaton.
The worst part?
You know there’s an API for most of this.
But setting up proper API automation for 20 different client sites?
That’s a week-long project.
By the time you finish, you could have just… clicked the buttons.
Until browser-using AI agents changed everything.
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July 2025: The ChatGPT Agent Launch That Changed Everything
I remember exactly where I was when OpenAI announced ChatGPT Agent.
Mid-morning.
Coffee getting cold while I watched the demo.
ChatGPT was… using a browser. Clicking buttons. Filling forms. Reading screens. Making decisions.
That’s when it hit me like a lightning bolt.
WordPress is entirely browser-based.
Every setting. Every configuration. Every update. It’s all just… clicking buttons in wp-admin.
If ChatGPT could use a browser, it could manage WordPress sites.
The possibilities exploded in my mind:
Bulk updates across client sites
WooCommerce store setups from scratch
Tax rate changes across multiple installations
Security configurations replicated perfectly
Plugin compatibility testing on staging sites
Everything I’d been doing manually could be delegated to an AI that never gets tired, never makes typos, and never accidentally clicks the wrong button at 11:40 p.m.
And ChatGPT wasn’t alone.
There are a few more browser-using AI agents in the market like Perplexity Comet and Claude for Chrome.
The game had changed.
WordPress automation that was impossible six months ago was suddenly trivial.
I immediately started experimenting.
And what I discovered changed my entire business.
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How Browser-Using AI Agents Actually Work
A browser-using AI agent is exactly what it sounds like – an AI that can use a browser like a human.
It can:
Open websites and navigate pages
Read what’s on the screen
Click buttons and links
Fill out forms
Take screenshots
Document what it did
But here’s the killer feature for WordPress:
It uses the same wp-admin interface you already know.
No API setup. No authentication headaches. No custom code.
Picture this scenario:
The government just announced a tax rate change from 5% to 7%.
You have 20 WooCommerce client sites.
Each site requires:
Log in to wp-admin
Navigate to WooCommerce → Settings
Click Tax tab
Click Standard Rates
Find the rate field
Change 5% to 7%
Click Save
That’s 7 clicks per site. 20 sites = 140 clicks.
Two hours of your life.
Gone.
Doing the exact same sequence over and over.
Now watch what happens with an AI agent:
You tell it once: “Update the tax rate from 5% to 7% on these 20 sites.”
It does exactly what you would do – logs in, navigates to WooCommerce → Tax → Standard Rates, changes the value, saves.
Across all 20 sites…. while you make coffee.
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The WooCommerce Setup That Convinced Me This Was Real
Let me show you what made me a believer.
Setting up a WooCommerce store from scratch involves:
That’s 100+ clicks and at least 5 hours of mind-numbing work.
Watch what happened when I gave an AI agent a simple checklist:
The result?
A fully configured WooCommerce store.
Every setting perfect. Every product created. Ready for orders.
100+ clicks. Zero human involvement after providing the checklist.
Want techniques like these weekly?
Join The Art of Vibe Coding—short, practical emails on shipping with AI (without the chaos).
No spam. Unsubscribe anytime. Seriously.
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The Secret: It’s All About The Checklist
Here’s what nobody tells you about AI automation:
Your AI agent is only as good as your checklist.
But here’s the beautiful part – you already have these checklists.
They’re in your SOPs.
Your client documentation.
Your head (you’ve done this 100 times).
The even better news?
AI can help you create perfect checklists in two revolutionary ways.
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Method #1: Let AI Interview You (The Clarifying Questions Technique)
This changed my entire workflow.
Instead of writing a 200-line checklist from scratch, I give the AI a rough idea and let it interview me.
Here’s exactly how it works:
Step 1: Give AI Your Rough Concept
The full “Let AI Interview You” prompt:
I want to create a demo site for a WooCommerce website. The data for this site needs to be Malaysia-centric, as I am based there.
To achieve this, we will need to set up several key elements: the business address, tax, shipping options, and approximately five products. We'll focus on selling physical products, specifically t-shirts.
For tax, we need to use the local Malaysian SST. Regarding shipping, we will configure it for the US, UK, Singapore, and Malaysia.
For payments, I initially considered a gateway that offers a sandbox environment. However, for now, let's proceed with Stripe as the payment gateway.
So, to summarize, we'll have the address, tax, shipping settings, payment gateway, and physical products (t-shirts) configured for the countries mentioned.
My request from you is a comprehensive list of all necessary items and data points I can use to populate the WooCommerce website, including specific details for the address, tax, shipping, and any other relevant information you deem necessary.
Ask me clarifying questions until you are 95% confident you can complete this task successfully.
a. If the question is about choosing different options, please provide me with a list of options to choose from. Mark the option with a clear label, like a, b, c, etc.
b. If the question need custom input that is not in the list of options, please ask me to provide the custom input.
Always mark each question with a number, like 1/, 2/, 3/, etc. so that I can easily refer to the question number when I answer.
For each question, add your recommendation (with reason why) below the options. This would help me in making a better decision.
Step 2: AI Asks Clarifying Questions
Look at what the AI does. It doesn’t just ask “what do you want?” It provides specific options:
1/ Brand for the demo store
a) BajuLab (fictional)
- Recommendation: Great for demos; avoids mixing with your real brand.
b) Smashing Advantage Apparel (uses your existing brand umbrella)
- Recommendation: Useful if you'll demo to your clients as "your studio's" store.
c) Custom brand (tell me the exact name)
Every question has:
Clear options (a, b, c)
Recommendations with reasoning
Custom input if needed
The AI continues with granular questions about:
Tax configuration (SST vs GST)
Shipping zones and methods
Payment gateway preferences
Product catalog structure
Step 3: Answer With Simple Choices
My responses? Dead simple:
No lengthy explanations, or complex configurations. Just pick from the menu.
Step 4: AI Generates Perfect Checklist
The AI transforms my simple answers into a comprehensive, copy-paste kit:
But, it’s not done yet.
Next, we will ask the AI to convert it into a detailed checklist (with click-path + values):
From vague idea to perfect checklist in 10 minutes.
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Method #2: Turn Your Screen Recording Into a Checklist
Already know how to do the task?
This method is even faster.
Step 1: Record Yourself Doing It Once
Open your screen recorder. Do the task once. Don’t explain anything – just do it normally.
Step 2: Upload to AI
Drop your video into Google AI Studio. No editing needed.
The “Turn Your Screen Recording Into a Checklist” prompt:
I've uploaded a video demonstrating the WordPress plugin update process. The video covers:
1. Creating a backup using a backup plugin to protect against issues
2. Navigating to the plugins page and identifying plugins that need updates
3. Updating plugins one-by-one and verifying the site after each update
4. Restoring from backup if problems occur
**Your Task:**
Analyze the video thoroughly and create a **comprehensive, step-by-step checklist** for updating WordPress plugins. This checklist must be:
- **Generic and reusable** - Don't reference specific plugin names; use placeholders like "[Plugin Name]" or "[Backup Plugin]"
- **Detailed with exact click-paths** - Format: `Dashboard > Section > Subsection > Button/Field`
- **Include specific values** - Note any settings, options, or fields that need to be filled/selected
- **Action-oriented** - Each step should start with a clear action verb (Click, Navigate, Select, Enter, Verify, etc.)
- **Include verification steps** - After each critical action, specify what to check/verify
- **Organized by phases** - Group steps into: Preparation/Backup, Pre-Update Checks, Update Process, Post-Update Verification, and Troubleshooting/Restore
**Format the checklist as:**
- [ ] **Step description** → `Click path` → Value/Action → ✓ Verification checkpoint
This checklist should enable anyone to replicate the process consistently across different WordPress sites and various plugins.
Step 3: AI Documents Every Click
The AI watches your video and creates step-by-step documentation:
Your muscle memory becomes reusable documentation.
Every click. Every navigation. Every setting.
Captured and structured automatically.
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From “Update and Pray” to “Update and Relax”
WordPress updates.
We all know the feeling.
That red notification bubble haunting your admin bar: “12 updates available.”
Your stomach drops.
Because you know what’s coming.
The Official Best Practice:
Make a full backup
Update on staging first
Test all functionality
Check for conflicts
Document changes
Then update production
The Reality at 11:40 p.m.: “Select all → Update → Pray”
We’ve all been there.
Rolling the dice.
Hoping this isn’t the update that breaks everything.
Remember the last time an update went wrong?
The payment gateway that stopped working (discovered by an angry customer)
The contact form that suddenly disappeared
The shipping calculator that decided everything costs $0
The homepage that went completely blank
That “minor” security update that broke your custom CSS
The recovery process is its own special hell:
Frantically googling error messages
SSH-ing into the server at midnight
Trying to remember which backup actually works
Explaining to the client why their site was down for 3 hours
Promising yourself you’ll “do it properly next time”
But next time comes, and you’re exhausted, and there are 12 sites to update, and…
“Select all → Update → Pray”
What if I told you an AI agent could handle all of this automatically?
Not just the updates.
The entire process:
The Challenge: Update multiple WordPress plugins across a client site The Twist: If anything breaks, automatically rollback
Watch what happened:
The AI agent:
Created a backup first (verified it was complete)
Updated plugins systematically (one at a time)
Checked the site after each update (front-end and critical functions)
When it detected a broken plugin, deactivate it.
Documented exactly which plugin caused the issue
Sent a report with screenshots and recommendations
Total human involvement: Zero.
No stress. No late-night panic. No angry client calls.
Just a calm notification: “Updates completed. One compatibility issue detected and resolved. Full report attached.”
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“But Isn’t It Risky?” Let’s Talk Security
I know what you’re thinking.
“Giving an AI access to my WordPress sites? That sounds like a security nightmare.”
Valid concern. Let’s address it head-on.
Here’s the reality:
Your WordPress sites are already at risk every day from:
Plugin vulnerabilities (remember that massive security hole last month?)
Weak passwords (“password123” is still in your client’s database)
Human error (ever updated the wrong site at 11:40 p.m.?)
Outdated themes causing conflicts
That intern who has admin access for some reason
You already know how to handle these risks: backups, staging, testing, proper procedures.
With AI agents, we apply the exact same precautions.
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The Three Commandments of AI Agent Security
Rule #1: Never Give AI Your Actual Credentials
Never. Ever. EVER.
Instead:
Use one-time passwordless login links
Log in first, then hand control to AI
Store API keys in wp-config.php, not in checklists
Use placeholders in checklists, update manually after
Rule #2: AI Works on Staging, Not Production
Your workflow becomes:
AI agent does its work on staging
You review the results (30 seconds)
If good → merge to production (one click)
If broken → no harm done
Almost every decent host offers staging now. No excuses.
Rule #3: Always Make Backups First
This should already be your religion. But with AI agents, it’s scripture:
Backup before any major changes
Verify the backup actually works
Know your restoration process cold
These aren’t new risks. They’re the same risks you already manage.
The AI agent is just another tool. Like giving your assistant access to update plugins. Except this assistant never gets tired, never makes typos, and documents everything.
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The Hidden Benefits Nobody Talks About
Once I started using AI agents for WordPress tasks, unexpected benefits emerged:
Benefit #1: Perfect Documentation
Every task the AI performs is documented. Every click. Every change. Every result.
Client asks “what changed on my site last month?”
Here’s the exact log of every update, every setting change, every modification. With timestamps.
Benefit #2: Consistency Across All Sites
That security configuration you perfected? It’s now identical across all 20 client sites.
No more “I think I configured this one differently” or “Did I remember to add that firewall rule?”
Benefit #3: Testing Becomes Trivial
Want to test how a plugin update affects 5 different theme configurations?
Spin up 5 staging sites.
Give the AI agent the same checklist for each.
Watch it test all 5 simultaneously while you grab coffee.
Benefit #4: You Can Take a Real Vacation
Picture this: You’re on a beach in Bali. Feet in the sand. Favorite beverage in hand (mine’s a cold coconut water, but you do you).
Your phone buzzes. It’s your AI agent:
“Weekly maintenance complete. 47 plugins updated across 12 sites. Two compatibility issues detected and resolved. All sites passing health checks. Full report ready for client review.”
You take another sip. Maybe check the report. Maybe don’t.
Site maintenance doesn’t stop because you’re sipping margaritas on a beach. Your AI agents run on the cloud, execute your checklists, handle the issues.
You only intervene if something actually needs your expertise. Which, let’s be honest, is rare when it comes to routine updates.
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Your WordPress Workflow, Transformed
Let’s get specific about what this means for your daily workflow:
Before AI Agents (Your Current Week)
Monday: 3 hours of plugin updates across client sites
Tuesday: 2 hours setting up a new WooCommerce store
Wednesday: 1 hour updating tax rates and shipping zones
Thursday: 2 hours running security audits and fixing issues
Friday: 2 hours generating reports and maintenance
Total: 10 hours of clicking
After AI Agents (Your New Week)
Monday morning: 15 minutes reviewing AI agent reports from weekend maintenance
Monday afternoon: Actually building that custom feature your client wants
Tuesday – Thursday: Real development work
Friday: Planning next week while AI agents run all maintenance
Total: 15 minutes of oversight
That’s not a 50% improvement. That’s not even a 90% improvement.
That’s 40 hours per month of your life back.
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How to Start Tomorrow Morning
Here’s exactly how to implement this:
Step 1: Pick Your Most Painful Task
What did you do this week that made you want to quit WordPress?
Setting up another identical WooCommerce store?
Updating plugins across 10 sites?
Migrating settings between staging and production?
Pick ONE task. The one that hurts the most.
Step 2: Create Your First Checklist
Use Method #1 (AI interview) or Method #2 (screen recording).
Don’t overthink it.
Your first checklist won’t be perfect. That’s fine.
Step 3: Test on a Staging Site
Never test on production first. Clone one of your sites to staging.
Give the AI agent your checklist. Watch it work.
Step 4: Refine and Scale
Your first run will reveal issues:
Missing steps in the checklist
Unclear instructions
Edge cases you forgot
Fix them. Run again. Once it’s perfect, you have a reusable automation forever.
Step 5: Expand Systematically
One task automated successfully? Move to the next:
The Real Question: What Will You Do With Your Time?
Remember that Friday night at 11:40 p.m.? Clicking through client site number nine?
That version of you doesn’t have to exist anymore.
You didn’t become a WordPress developer to click buttons. You got into this to build things. To solve problems. To create.
When 80% of your routine work is handled by AI agents, what will you do?
Maybe you’ll:
Take on more clients (without burning out)
Build that SaaS you keep dreaming about
Actually enjoy your weekends
Learn that new framework you’ve been putting off
Spend time with your family instead of wp-admin
Or maybe you’ll just sleep better knowing that while you rest, your AI agents are handling the midnight maintenance, the weekend updates, the mind-numbing configurations.
All the work. None of the clicking.
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Your Challenge This Week
I’m not asking you to automate everything. I’m not even asking you to trust AI with production sites yet.
I’m asking you to do ONE thing:
Pick one repetitive task you did this week.
Just one.
Create a checklist for it. Hand it to an AI agent. Watch what happens.
Because once you see it work – once you watch an AI agent perfectly execute 100 clicks in 6 minutes – you’ll realize something profound:
You’ve been doing WordPress on hard mode this entire time.
The age of manual WordPress management is over.
The age of AI agents is here.
What will you automate first?
Stop clicking.
Start delegating.
Your future self – the one actually building cool stuff instead of clicking buttons – will thank you.
There are seemingly endless supply of content coming at us from all directions.
Trying to take in all the content out there can be really tough. It’s like trying to drink from a fire hydrant – way too much, way too fast! And let’s be real, it’s about as doable as trying to read every book in a huge library all at once.
Even though we’re often eager to soak up as much information as we can, it’s just not possible to read everything! With our busy schedules, finding time to get through lengthy articles, books, or podcasts can be a real tough cookie.
That’s where a summary of all that content come in handy.
Here’s why:
They help you get the main idea fast.
They make learning quick and save your time.
They’re useful when you’re short on time or need to decide whether the content is worth consuming.
In essence, summaries streamline our consumption of information, making it more efficient and effective.
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How AI Can Help Us in Summarizing Long Form Content
In recent years, AI has made some seriously impressive strides.
And guess what? It turns out AI can be a total game-changer when it comes to summarizing long-form content!
But it hasn’t always been smooth sailing.
When I first started using AI to summarize long-form content, I began with models like GPT-3.5 and the early release of GPT-4. Back then, the experience was quite challenging.
These older models had limited context windows, meaning they could only handle small chunks of content at a time. This often resulted in skipped or ignored sections, leaving me with a fragmented understanding of the material, much like trying to piece together a puzzle with half the pieces missing.
The summaries were often superficial, missing the depth needed for complex topics.
Over time, I noticed another significant issue: maintaining coherence.
These early models struggled with short-term memory, making it difficult for them to retain information over long passages. This led to summaries that were incoherent or disjointed, with no smooth flow. Additionally, when faced with lengthy content, these models couldn’t prioritize essential information effectively, resulting in summaries that missed the mark.
Thankfully, things have come a long way with the latest AI models….
Advancements with New LLM Models
Using newer versions like Claude 3 Opus, Gemini 1.5 Pro, and GPT-4o has been a night and day difference.
Larger Context Windows: .
Enhanced Processing Capabilities: These newer models have much larger context windows. They can process and understand entire documents without losing context, which is a huge relief. .
Comprehensive Summarization: Because they can look at the whole content at once, the summaries they produce are more comprehensive and accurate. No more missing pieces! .
Improved Recall Capabilities: .
Memory Integration: Modern models have much better memory integration. They can recall previously processed information more effectively, keeping track of key points throughout the summarization process. .
Context Retention: This improved memory means that important themes and details are retained, leading to coherent and thorough summaries. .
Depth and Detail in Summarization: .
In-Depth Analysis: These advanced models can perform deeper analysis, capturing intricate details and nuances. They dive deep into the content instead of just skimming the surface. .
Contextual Understanding: Larger context windows help these models grasp the broader context of the content, ensuring that summaries are not just a collection of isolated points but a coherent narrative that makes sense.
These new LLM models make it easier than ever to stay informed, save time, and make smarter decisions about what to read or watch.
Now that you’ve got a good idea of how AI has evolved and improved in summarizing long-form content, let’s get down to the nitty-gritty. After all, it’s not just about having a fancy AI models, but knowing how to use it effectively.
And the key to that? Crafting a good summarizing prompt.
Think of it as instructing a new intern.
You wouldn’t just throw them into the deep end without clear instructions, right? Same goes for AI. It’s all about giving it the right guidance to get the job done. Ready to learn how to craft the perfect summarizing prompt?
Let’s get started!
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What Makes a Good Summarizing Prompt?
Picture this: you’ve found a lengthy article that you want to summarize using AI.
You excitedly copy the text, paste it into your AI tool, and type out a quick prompt: “Please summarize the content above.” You hit enter, eager to see the magic happen.
But the result? It’s underwhelming, to say the least.
Here’s why:
Surface-Level Summaries: .
Such prompts tend to produce summaries that only scratch the surface, missing deeper insights.
Key examples, strategies, and important details are often omitted. .
Lack of Depth: .
Without specific instructions, the AI might not delve into the sub-points and nuances, leading to a shallow summary.
The summary might fail to capture the full context and intricacies of the content.
This is the problem with generic summarizing prompts.
They don’t provide the AI with any guidance on what aspects to focus on, how much detail to include, or how to structure the summary.
As a result, the AI does its best, but the output often misses the mark.
To get the best out of AI for summarizing stuff, we can’t just throw any old instructions at it.
We’ve gotta give it clear, detailed directions, kinda like how you’d explain a game to a friend. This helps the AI zero in on the important stuff, include all the juicy details, and arrange everything in a way that makes sense.
Doing this, we can use AI to create great summaries easily.
Consider the following prompt:
### Instructions for Crafting a Detailed Summary:
1. **Objective**:
Understand that the purpose of this summary is to extract the essential insights, strategies, examples, tactics, and tips from the content. The reader should gain key knowledge from your summary without having to read the entire content line by line.
2. **Length**:
While brevity is valued, it should not come at the expense of key information. It's better to have a longer, comprehensive summary than a brief one that misses out on crucial insights.
3. **Detailing Topics**:
- When summarizing a section, delve beyond just the headline. Dive into the sub-points and nuances.
- If specific examples are provided to illustrate a point, include those examples.
- If a particular strategy or tactic is mentioned, describe it briefly in your summary.
4. **Incorporate Direct Quotes**:
If there's a particularly impactful or insightful quote from the content, include it verbatim, ensuring you attribute it to the speaker or author.
5. **Use Bullet Points for Clarity**:
- Bullet points make content easier to scan and digest.
- For instance, if multiple strategies are discussed under a section, list each strategy as a separate bullet point with a brief description or example.
6. **Avoid Generalizations**:
Avoid phrases like "Various strategies are discussed." Instead, specify what those strategies are: "The content discusses strategies such as A, B, and C, explaining that..."
7. **Conclude with Takeaways**:
At the end of your summary, include a "Key Takeaways" section. This should be a bullet-pointed list that captures the core lessons, strategies, and insights from the content.
This prompt is my secret sauce.
It guides the AI to:
Set Goals: Tell the AI exactly what we want – to pull out the most important bits and strategies.
Dig Deep: Ask the AI to go beyond the main points and look at the little details and finer points.
Use Direct Quotes: Spice things up by adding in powerful quotes straight from the source.
Keep it Organized: Use bullet points to make everything clear and easy to read.
Stay Specific: Make sure the AI doesn’t just make broad statements, but gives us the specific details.
Recap Quickly: Wrap things up at the end with the key lessons for a quick and easy reference.
With a prompt like this, the AI has a clear roadmap to follow.
The resulting summary is detailed, insightful, and captures the core of the original content. It’s a summary that actually saves you time and provides value.
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Comparing GPT-4o, Gemini 1.5 Pro, and Claude 3 on Summarizing Long-Form Content
Imagine this: you’ve found a gem of a podcast episode, but it’s way too long.
Tim Ferriss’s podcast episodes are like buried treasures full of insights. The problem? They’re longer than your usual commute. That’s where AI summarizers can help.
In a test, we had three leading AI models summarize an episode.
It’s a great episode on How to Say “No,” Market Like a Professional, and Win at Life. We tested GPT-4o, Gemini 1.5 Pro, and Claude 3, using the same detailed prompt.
And guess what? The results were fascinating.
GPT-4o: Detailed and Organized
First, we tell GPT-4o that its role by providing a detailed prompt for summarizing Seth Godin’s podcast episode, including objectives and key instructions.
Here we feed GPT-4o with the podcast transcript
Here’s GPT-4o’s summary of Seth Godin’s podcast on the Tim Ferriss Show, highlighting key insights on habits, routines, and effective marketing strategies.
GPT-4o Summary of Seth Godin’s Podcast (Click to Expand)
### Detailed Summary of Tim Ferriss and Seth Godin's Discussion
**Introduction:**
- Tim Ferriss introduces Seth Godin, highlighting his prolific career, numerous bestsellers, and influential blog. Godin's newest book, *This is Marketing*, is featured.
- Seth Godin has been a pivotal figure in marketing, strategic quitting, leadership, and challenging the status quo.
**Dealing with Overwhelm:**
- Seth discusses how he manages overwhelm, emphasizing the importance of setting personal boundaries.
- He shares strategies like not attending meetings, avoiding TV and social media, which free up significant time.
- Seth highlights the systemic nature of overwhelm, likening it to drinking from a firehose, and suggests controlling inputs to manage it.
**Managing Projects and Priorities:**
- Seth shares his philosophy on choosing projects, stressing the importance of saying no to maintain focus.
- He believes in the concept of "enoughness" and avoids making decisions based on a fear of insufficiency.
- Seth emphasizes the need for intentional decision-making rather than short-term maximization, drawing from his experiences as an unemployed individual making choices over 28 years.
**Long Work vs. Hard Work:**
- Seth defines long work as continuous, low-value tasks and hard work as emotionally challenging, high-value efforts.
- He encourages focusing on hard work, which involves creativity, risk, and generosity.
- Examples include his early speaking gigs and the development of altMBA, both of which required perseverance through initial unpopularity.
**Creating altMBA:**
- Seth recounts the inception of altMBA, a project born from a deep contemplation of how to teach effectively in a digital age.
- He highlights the importance of confronting fear and being willing to test and iterate on new ideas.
- The success of altMBA came from focusing on impactful, transformational education rather than broad, shallow engagement.
**Boundaries and Self-Management:**
- Seth shares practical tips for maintaining boundaries, such as using TextExpander to manage responses efficiently.
- He believes in setting clear rules about what he will and won’t do, thus avoiding decision fatigue.
- Authenticity is seen as secondary to professionalism; consistency and reliability are prioritized over situational authenticity.
**Smallest Viable Audience:**
- Seth advocates for targeting the smallest viable audience, explaining that successful businesses focus on specific, dedicated groups rather than the masses.
- He uses examples like Penguin Magic and charity: water to illustrate how niching down can lead to significant impact and success.
- By focusing on a defined audience, businesses can create deeper connections and more meaningful products or services.
**Pricing and Scarcity:**
- Seth discusses the importance of charging appropriately for value, using examples like Supreme and Franklin Barbecue to demonstrate how scarcity and high pricing can create demand and prestige.
- He argues that low pricing often undermines perceived value and that businesses should aim to provide exceptional experiences that justify higher prices.
**Case Studies and Practical Applications:**
- Seth shares insights into various successful businesses, highlighting how they leverage scarcity, storytelling, and exceptional customer experiences to thrive.
- He emphasizes the need to understand and serve specific customer needs and dreams to build lasting success.
**Empathy and Professionalism:**
- Seth underscores the importance of empathy in marketing, encouraging professionals to view themselves as teachers helping their audience achieve their goals.
- He advises against the fear of rejection and encourages focusing on serving a specific group generously and consistently.
**Conclusion and Resources:**
- Seth provides recommendations for further reading, including books by Scott Harrison, Kat Hoke, Jim Ziolkowski, Jacqueline Novogratz, and Shawn Askinosie, which offer valuable insights into creating impactful work.
- Tim and Seth discuss additional resources and where to find more of Seth’s work, including his blog, new book, and altMBA program.
**Key Takeaways:**
- **Overwhelm Management:** Set personal boundaries and prioritize essential tasks.
- **Project Selection:** Focus on impactful projects and say no to distractions.
- **Work Philosophy:** Embrace hard work that challenges you emotionally and creatively.
- **Smallest Viable Audience:** Target specific, dedicated groups for deeper connections and success.
- **Pricing Strategy:** Charge appropriately to reflect value and create scarcity.
- **Empathy in Marketing:** View yourself as a teacher and serve your audience’s needs.
- **Consistency and Professionalism:** Maintain consistent standards and prioritize reliability over situational authenticity.
By applying these insights, strategies, and examples, individuals and businesses can enhance their effectiveness, create meaningful connections, and achieve lasting success.
GPT-4o delivered a killer summary of Seth Godin’s podcast episode.
It started off strong with a clear introduction, then dove into the meaty topics. It tackled things like overcoming overwhelm, project management, and the difference between long work and hard work.
The real highlight was GPT-4o’s knack for detail. It gave a great explanation of Seth’s thoughts on pricing and scarcity, using relatable examples like Supreme and Franklin Barbecue. It didn’t stop at the surface, it went deep into the content, giving us a clear understanding of the topics.
At the end, GPT-4o wrapped it all up with a tidy list of key takeaways. This made it super easy to get the core lessons without having to listen to the whole podcast.
In short, GPT-4o nailed the summary game.
Gemini 1.5 Pro: Insightful and Engaging
Here, we provide a detailed prompt to Gemini 1.5 Pro via the Gemini API playground for summarizing Seth Godin’s podcast episode
Gemini 1.5 Pro’s engaging summary of Seth Godin’s podcast on the Tim Ferriss Show, highlighting key insights on managing overwhelm and choosing work.
Gemini 1.5 Pro Summary of Seth Godin’s Podcast (Click to Expand)
## Capturing the Essence of Tim Ferriss and Seth Godin on Overwhelm, Marketing, and Doing the Work
This detailed summary unpacks the insightful conversation between Tim Ferriss and marketing guru Seth Godin. They delve into topics like managing overwhelm, choosing the right projects, the power of saying no, and the nuances of effective marketing in today's world.
**Navigating Overwhelm and Choosing Your Work:**
- **The Shame of Overwhelm:** Godin highlights the feeling of shame associated with overwhelm, especially when many people lack choices and opportunities. He emphasizes that it's a systems problem, akin to trying to quench your thirst by drinking from a firehose.
- **Creating Boundaries:** Godin stresses the importance of setting personal boundaries to combat information overload. He advocates for freeing up six hours a day by eliminating unnecessary meetings, television, and social media consumption. He suggests a zero-based budgeting approach to add back only essential activities.
- **The World's Worst Boss:** Godin reminds us that we are our own worst bosses. We often mismanage our time and energy, failing to prioritize self-development and making poor choices about the work we take on.
- **Long Work vs. Hard Work:** Godin differentiates between "long work" – grinding away for hours on end – and "hard work" – the emotional labor of confronting risk, embracing generosity, and discerning nuance. He argues that hard work, though often less immediately rewarding, ultimately leads to greater satisfaction and the creation of lasting assets.
- **Choosing Hard Work:** Godin shares examples from his own life where he chose hard work over instant gratification, such as giving speeches for free in the early days of his career and persisting with the altMBA even when it wasn't an immediate hit. He emphasizes the importance of playing the long game and building a sustainable career.
**The Power of No and Taming the Lizard Brain:**
- **The Lizard Brain's Influence:** Godin explains that our subconscious drives most of our decisions, often rooted in fear and insecurity. He encourages listeners to recognize this influence and develop strategies to overcome it.
- **The Art of Saying No:** For those who struggle to decline requests, Godin suggests crafting a thoughtful and polite "no" template using tools like TextExpander. This pre-written response allows for efficiency and emotional regulation when declining requests.
- **Handwashing Equivalents:** Godin emphasizes the importance of establishing clear boundaries and sticking to them, much like a surgeon who always washes their hands. He shares his own "handwashing" equivalent – defining what he'll do for free versus what he charges for, eliminating the need for constant negotiation.
**The Smallest Viable Audience and the Power of Niche:**
- **Shifting from Mass to Niche:** Godin challenges the traditional marketing approach of targeting the masses. He argues that finding your "smallest viable audience" – a group deeply passionate about what you offer – is far more effective in today's fragmented media landscape.
- **Embracing Constraints:** He uses the example of noma, a world-renowned restaurant in Copenhagen, to illustrate how embracing constraints can lead to a highly sought-after experience. noma's commitment to local sourcing and its high price point act as filters, attracting only those who truly value their unique offering.
- **Examples of Niche Success:** Godin highlights companies like Penguin Magic and charity: water, which have thrived by focusing on specific, passionate audiences. He emphasizes that serving a niche requires deep empathy and a willingness to truly understand and meet their needs.
**The Three-Sentence Marketing Promise and the Importance of Story:**
- **Defining Your Promise:** Godin introduces his three-sentence marketing promise template:
- "My product is for people who believe _blank_."
- "I will focus on people who want _blank_."
- "I promise engaging with what I make will help you get _blank_."
- **Selling the Story, Not Just the Product:** He stresses the importance of crafting a compelling narrative around your product or service. People don't buy mere products; they buy the story, the feeling, and the status associated with them.
- **Supreme and the Power of Scarcity:** Godin dissects the success of Supreme, a clothing brand known for its high prices and limited-edition releases. He argues that Supreme has masterfully leveraged scarcity and social proof to create a sense of exclusivity and desirability.
**Key Takeaways:**
- **Embrace Hard Work, Not Just Long Hours:** Focus on the effort that creates lasting value, even if it's not instantly gratifying.
- **Master the Art of Saying No:** Set clear boundaries and stick to them to avoid overwhelm and prioritize what truly matters.
- **Find Your Smallest Viable Audience:** Identify the group that deeply resonates with your work and tailor your efforts to serve them exceptionally well.
- **Craft a Compelling Marketing Promise:** Clearly articulate who your product is for, what they desire, and how you'll help them achieve it.
- **Remember the Power of Story:** People buy into narratives. Create an experience and tell a story that resonates with your target audience.
This conversation offers a masterclass in navigating the complexities of modern work and marketing. By embracing hard work, focusing on a niche audience, and crafting a compelling story, we can create work that truly matters and resonates with the people we seek to serve.
Gemini 1.5 Pro turns summarizing into storytelling.
This model kicks things up a notch by delivering a comprehensive summary that reads like a captivating narrative. It sets the tone right from the start, presenting the discussion as a masterclass in modern work and marketing, drawing the reader in. The summary is neatly divided into distinct sections, each honing in on a crucial theme – from overcoming overwhelm to the art of saying “no” and identifying your niche audience. What really sets it apart are the catchy subheadings like “The Lizard Brain’s Influence” and “The Three-Sentence Marketing Promise,” that make the summary not just informative but also enjoyable to read.
To top it off, Gemini 1.5 Pro serves up practical templates like Seth’s three-sentence marketing promise, offering readers not just insights but actionable takeaways.
In short, Gemini 1.5 Pro doesn’t just summarize, it tells a story.
Claude 3: Concise yet Comprehensive
An example of a detailed prompt given to Claude 3 for summarizing Seth Godin’s podcast episode on the Tim Ferriss Show.
Claude 3’s summary of Seth Godin’s podcast episode on the Tim Ferriss Show, showcasing its ability to produce concise yet comprehensive summaries
Claude 3 Summary of Seth Godin’s Podcast (Click to Expand)
Here is a detailed summary of the content, following the provided instructions:
### Summary of Tim Ferriss Podcast Episode with Seth Godin
#### On Dealing with Overwhelm and Setting Boundaries
- Overwhelm often comes with shame, feeling that you have too many choices/inputs while others have insufficient.
- To deal with overwhelm, set your own boundaries. Clear out 6 hours a day by eliminating meetings, TV, social media.
- Seth's rule: "I don't go to meetings. I don't watch television. I don't look at Facebook or Twitter." Eliminating these frees up significant time.
- Feeling behind can also cause overwhelm. Confront the discomfort to get to the other side.
- As Seth puts it: "Sitting with an uncomfortable input when we don't care enough to make things better is just a formula to be unhappy."
#### On Choosing Projects and Saying No
- Seth decided to be a teacher, not run a software company, to have most impact. Took time in the desert to decide: "I'm going to come back from this trip and either say, 'I have a thing' or I'm going to say, 'I'm walking away from that medium entirely.'"
- Showed his 'altMBA' idea to people; half didn't get it. "Now I'm on to something. Because if everyone said it was a good idea, it was probably banal."
- Seth is "not in the altMBA. There are no videos of me. I'm not teaching it. I just built the system."
- "The choices that I've made of saying yes or saying no are at the heart of the career. It's not the work, as much as it is deciding to do the work, and deciding what work not to do."
#### The World's Worst Boss and Self-Management
- "Authenticity is totally overrated. I don't want an authentic surgeon who says, 'I don't really feel like doing knee surgery today.' I want a professional who shows up whatever they feel like." - Seth Godin
- Seth decided early on to be the person who never misses a deadline, with strict rules. "I made a decision, a very, very long time ago, probably when I was 18 or 20, where I said, 'Look, there's a whole bunch of work I'm just not willing to do...So here's what I'm going to be. I'm going to be the person who never misses a deadline.'"
#### Finding Your Smallest Viable Audience
- "If this isn't your first Tim Ferriss podcast, you are a fan of Tim and the way he engages with the world. And yet, 99 percent of the people on planet Earth have never heard of Tim Ferriss...How can those two things coexist?" - Seth Godin
- Find the guts to say: "There are 250 people who care about tilt shift lenses as much as I do, and I'm going to make a tilt shift lens for those 250 people that changes their life."
- Specific example: Penguin Magic, a multi-million dollar company serving amateur magicians' need for new tricks, even though most don't know of them.
- Avoid trying to reach the masses. Go where people's attention already is.
#### Non-Marketing Books That Teach Great Marketing
- Scott Harrison's "Thirst" about charity: water
- Kat Hoke's "Second Chance"
- Jim Ziolkowski's "A Walk in Their Shoes" about changing underprivileged kids' lives
- Jacqueline Novogratz's "The Blue Sweater" about Acumen Fund
- Shawn Askinosie's "Meaningful Work" on his chocolate company
- Seth: "When you read them, you can apply 90 percent of what they're talking about to building your for-profit enterprise, because it's all the same. It's value. Who are you creating value for? And why are you creating that value?"
### Key Takeaways
- Set boundaries and eliminate non-essential activities to deal with overwhelm.
- Choosing what to say yes or no to is at the heart of a successful career.
- Be a professional, not necessarily "authentic." Stick to your commitments.
- Find your smallest viable audience - the specific group that really needs what you offer.
- Lessons on creating value can be learned even from non-business books.
Claude 3 pumps out the most concise summaries, but don’t let that fool you.
Each section is clearly divided, with bold headings that perfectly capture the essence of the main ideas. Its brevity doesn’t mean it’s skimping on the good stuff – far from it. Key examples and direct quotes are all part of the package, shedding light on things like Seth’s approach to overwhelm and his views on authenticity versus professionalism. At the tail end of it all, a “Key Takeaways” section steps in, boiling down the big lessons into clear, actionable nuggets of wisdom.
So, while it may be the most concise, Claude 3 gets you straight to the good stuff without missing a beat.
The Verdict
All three models produced excellent summaries that capture the podcast’s core insights.
GPT-4o’s summary is the most detailed, making it perfect for those who want an in-depth understanding. Gemini 1.5 Pro shines with its engaging, narrative style, making the content enjoyable to read. Claude 3 excels in conciseness, providing key points without unnecessary fluff.
Ultimately, your choice depends on whether you prioritize detail, engagement, or brevity.
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Deep Dive Summarization by Focusing on a Specific Topic
Summarizing extensive content like three-hour podcasts can be challenging, even for advanced AI.
The sheer volume of information can lead to summaries that only skim the surface, leaving crucial insights untouched. But hey, we’re not settling for that. We’re aiming for detailed, insightful summaries that dig deep into specific topics, ensuring we don’t miss a thing.
So, let’s dive in and learn how guiding AI to focus on specific topics can transform our summaries and bring us the rich insights we’re after.
How to Extract and Summarize Specific Topics
Step 1: Extract Relevant Content
First off, figure out the part of the content you’re really into.
For instance, if you’re curious about Seth Godin’s discussion on “Long Work vs. Hard Work” from his Tim Ferriss interview. Just grab that bit from the transcript.
Here’s how: Ask the AI to extract the segment where Seth talks about this topic. A simple query like, “Can you extract the part of the transcript where Seth Godin talks about the long work vs hard work?” should do the trick.
The AI might come back with something like this:
Step 2: Crafting a Detailed Summarizing Prompt
After you’ve pulled out the content you’re really into, it’s time to get the AI to work its magic.
You’ll want it to use the same super detailed summarization instructions we used earlier. But here’s the twist: you’re gonna ask it to only focus on the part of the transcript that’s all about the topic you’re dying to dive into.
Tell the AI:
Detailed Summary Example
Here’s an example of how the AI might respond with a detailed summary of the “Long Work vs. Hard Work” section:
### Detailed Summary of the Long Work vs. Hard Work Section
**Objective**:
This section focuses on the differences between long work and hard work, with Seth Godin explaining the concepts and providing examples from his experience.
**Main Points**:
1. **Definitions**:
- **Long Work**: Involves tasks that are time-consuming and repetitive, such as billing hours for a lawyer or performing piecemeal work. These tasks are exhausting and provide diminishing returns as more time is spent.
- **Hard Work**: Consists of emotional labor, confronting risk, and finding generosity. This type of work requires dealing with discomfort and seeing nuance. Hard work is about creating value through challenging tasks that don't always provide immediate applause or satisfaction.
2. **Examples and Insights**:
- **Platform Work**: Platforms like Fiverr and Medium often reward long work, where additional hours don’t yield significantly better results.
- **Emotional Labor**: Hard work involves emotional labor that might feel risky but is ultimately generous and useful over time.
- **Seth's Experience**:
- Early in his speaking career, Seth paid to give the first hundred speeches and was initially ranked very low among speakers. He faced getting booed off stage multiple times.
- Starting the altMBA and other online initiatives involved significant hard work. Initially, these projects were not well-received, requiring persistence and the willingness to "make a mess" before finding success.
- He described a critical moment of reflection in the desert where he decided whether to continue pursuing the altMBA concept or walk away. He emphasized the importance of making decisions, even risky ones, to eventually produce something valuable.
3. **Strategies and Tactics**:
- **Focusing on Impact**: Seth stresses the importance of choosing projects that align with one's long-term goals and values, rather than being driven by immediate opportunities or fears of insufficiency.
- **Iterative Process**: The altMBA was developed through iterative playtesting, with Seth pretending to be the system. This allowed for refinement based on real feedback before fully committing to the project.
- **Building Trust**: Initially, the altMBA participants were those who gave Seth the benefit of the doubt, highlighting the importance of trust and reputation in launching new initiatives.
4. **Direct Quotes**:
- "Hard work is the emotional labor of confronting risk; the emotional labor of finding generosity when you don’t feel like it; the emotional risk of seeing nuance where there isn’t a lot of nuance." - Seth Godin
- "You have to get booed off stage a whole bunch of times." - Seth Godin, on the journey to becoming a successful speaker.
**Key Takeaways**:
- **Long Work vs. Hard Work**: Understand the distinction and focus on hard work that involves emotional labor and risk-taking to create lasting value.
- **Persistence**: Success often requires enduring initial failures and continuously refining your approach.
- **Strategic Choices**: Make decisions that align with your long-term goals and values, and avoid being driven by fear of insufficiency.
- **Building Trust**: Cultivate trust and reputation to help launch and sustain new projects.
- **Iterative Development**: Use feedback and iterative testing to refine projects before full-scale implementation.
And there you have it!
Zero in on the bits you care about the most and really dive into the nitty-gritty. This way, you save time and make sure you fully understand the stuff that matters to you.
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Wrapping It All Up
Managing the vast amount of information we encounter daily can feel overwhelming.
Fortunately, AI is here to help. With significant advancements in AI technology, models like GPT-4o, Gemini 1.5 Pro, and Claude 3 have transformed the way we summarize long-form content. These models offer larger context windows, better recall capabilities, and more detailed summarizations, making it easier to grasp main ideas quickly and efficiently.
Crafting the right prompt is key.
Just like giving clear instructions to a new intern, a specific and detailed prompt can guide the AI to produce comprehensive and insightful summaries.
Whether you need an in-depth understanding, an engaging narrative, or a concise overview, these AI models cater to various needs effectively. Don’t let lengthy content overwhelm you. Next time you face a long article, book, or podcast, remember that AI can help you digest and understand the content like a pro.
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Ever wonder how a magic trick works?
Well, building a website with Custom GPT AI is a bit like that, but I’ll let you in on the secret. Custom GPT is like your digital magician’s assistant, only it’s super-smart and knows a ton about WordPress.
Here’s the cool part: Custom GPT AI can write articles, choose pictures, and even come up with new ideas for your site. It’s like having a little wizard in your computer who makes website stuff easy and fun. But how does this magic happen? That’s where the WordPress REST API comes in. Think of it as the magic wand that lets Custom GPT AI talk to your WordPress site.
So, imagine you want to add something new to your site. In the old days, that would mean lots of typing and clicking. But now, you just chat with your personal AI WordPress assistant. You say, “Hey, I want a new blog post about productivity.”
And just like that, it starts working on it.
But here’s where it gets even more amazing.
Your AI assistant doesn’t just write the post; it uses the WordPress REST API to put that post on your site. You don’t have to worry about the hows and whys. It’s like telling your friend what you want for your birthday, and they surprise you with the perfect gift.
All this wizardry happens behind the scenes.
You tell AI what you need, and it figures out all the tricky bits. Then it reaches out to your WordPress site through the API and gets the job done. It’s like having a conversation with someone who makes your ideas come to life.
In short, Custom GPT AI is your friendly neighborhood web wizard.
It’s here to make managing your WordPress site as easy as chatting with a friend. No more getting lost in menus or scratching your head over complicated settings.
Just you, your ideas, and a bit of AI magic.
💡 Before diving into the setup process, it’s important to note that you must have a ChatGPT Plus or Enterprise account to use Custom GPT.
Settings up Custom GPT
Many believe that setting up advanced AI for WordPress is only for tech experts, but I’m here to shatter that myth.
Creating your own custom ChatGPT is simpler than you think.
1. Creating Your Custom GPT
First, log into ChatGPT, go to the GPT builder, and click the “Create a GPT” button.
Then, navigate to the “Configure” tab:
Here, you can easily create a new GPT by entering your specific instructions, where you tell the AI exactly what you want it to do.
2. Adding Instructions to Your Custom GPT
After creating your GPT, the next step is to configure it to meet your specific needs:
Name: Give your custom GPT a cool name!
Description: Jot down what your custom GPT is all about.
Instructions: Here’s the fun part – you get to lay down the rules! Tell your custom GPT how you want it to behave.
Here are the specifics I used for my own custom GPT:
Name: WP AI Assistant
Description: Expert WordPress assistant with REST API skills.
Instructions:
WP AI Assistant is designed to be a specialized AI assistant for managing WordPress sites, leveraging its expertise in WordPress functionalities and the WordPress REST API. Its primary role is to assist users in various WordPress-related tasks, such as installing plugins, creating posts, and performing other site management activities. It possesses a thorough understanding of WordPress, including themes, plugins, and general website administration. This AI is capable of guiding users through different tasks, offering practical advice, and executing actions through the WordPress REST API. It should maintain a helpful and informative tone, focusing on providing clear, step-by-step guidance to users.
3. Powering Up Your Custom GPT
Remember, your custom GPT isn’t just good at basic tasks.
By default, this handy AI helper is all set to surf the web and create images with DALL-E. But guess what? It can do even more! Want it to execute code? No sweat! Just give that “Code Interpreter” option a click and you’re good to go!
4. Link Your GPT to Your WordPress Site
Now, here’s the fun part!
You’re about to connect your Custom GPT to your WordPress site using the WordPress REST API. This is like making a secret handshake between your AI and your site. And the coolest part? It gives your AI the power to understand what you want and get things done on your site.
Managing your site just got a whole lot easier!
Ready to connect your Custom GPT to your WordPress site? Let’s get started!
Step 1: Create a new action
First up, hit the “Create new action” button.
Step 2: Add OpenAPI schema
Next, you’ll see a box labeled “Schema”. Drop the following OpenAPI schema in there:
Just remember to swap out the placeholder URL with the actual one for your website.
Step 3: Create an Application Password
Now, let’s get your GPT and WordPress site talking!
Head over to your WordPress admin dashboard and hover over your username in the top right corner. Click on “Edit Profile”.
Scroll all the way down to the “Application Passwords” section. Give your application password a name and hit the “Add New Application Password” button.
You’ll see a generated application password pop up. Make sure to copy and save it somewhere!
Step 4: Create Basic Authentication token
Next stop: base64encode.org. You’ll need to insert your WordPress username and the application password you just generated.
Let’s say, for instance, your username is admin and your application password is 2CnQ Z41k kI1b 2edl YO6j hElq. You’ll enter admin:2CnQZ41kkI1b2edlYO6jhElq into the box (note the : separating the username and password, and no spaces in the password). Go ahead and hit the “Encode” button to convert your string into base64 format.
You’ll get something like this: YWRtaW46MkNuUV0MWtrSTFiMmVkbFIPNmpoRWxx.
Copy this token and head back to your Custom GPT configuration page.
Step 5: Add Basic Authentication Token
Last step!
Click the “gear icon” button under the “Authentication” section.
Choose “API Key” as the Authentication Type. Then, paste in your token (example: YWRtaW46MkNuUV0MWtrSTFiMmVkbFIPNmpoRWxx) into the “API key” box.
Be sure to select “Basic” as your “Auth Type”. And… done!
Click “Save” to finish up.
5. Save & Publish your Custom GPT
All right, that’s a wrap on the setup process.
See that “Save” button up there on the top right? Give it a click to save all your hard work.
Oh, and one more thing. You’ll notice you can share this GPT with anyone who has the link. But, between you and me, it’s probably best to keep it to yourself. After all, we wouldn’t want this falling into the wrong hands, would we?
Effortless Plugin Management with Your AI WordPress Assistant
Now that everything is in place, it’s time to test out your AI WordPress assistant.
You won’t believe how easy it is to manage your WordPress plugins with just a few simple AI commands.
To install multiple plugins at once, you might use a prompt like:
Your Custom GPT assistant will understand this command and proceed to install these plugins for you.
Once installed, activating them is just as straightforward. You can say, “Can you help me activate all these plugins”
And just like that, these plugins will be up and running.
But the capabilities don’t stop there.
You can also update any plugin by instructing, “Update the Jetpack plugin to the latest version.” Similarly, if you need to remove a plugin, a simple command like, “Delete the Akismet plugin from my WordPress site,” will do the job.
With these simple commands, Custom GPT transforms the way you manage plugins, turning a potentially time-consuming task into a quick and effortless process.
Transforming Ideas into Engaging Posts with Your AI WordPress Assistant
Imagine having a friend who’s not just super smart but also loves writing.
That’s what it’s like when you use your personal AI WordPress assistant. This AI isn’t just any friend; it’s your creative partner, ready to help turn your thoughts into amazing blog posts.
First, let’s talk about ideas.
Ever been stuck, not knowing what to write about? Just ask your AI buddy. Say something like:
Pick one idea and let’s get writing.
But here’s the magic part – you’re not writing alone. Tell your AI, “Write a 400 ~ 500 words atomic essay focusing on <IDEA>.” Before you know it, there’s a whole blog post ready on your screen. It’s like having a super-fast writer who never gets tired.
Alright, your post is all set and ready to roll.
It’s time to share your masterpiece with the world.
No need to navigate through menus and settings. A simple command like, “Publish this post to my site,” and your AI does the rest. It’s as easy as telling your friend to pass the salt.
Elevate Your WordPress Site with Stunning DALL-E Generated Images
Guess what?
Your AI buddy isn’t just good at making content, it’s got a whole lot more tricks up its sleeve!
Let’s say you’ve just written a great post about productivity hacks for solo entrepreneurs. You need a killer featured image to really make it stand out. No problem! Just tell your AI, “I need an image that captures the heart of the post”. And bam! Your AI creates an image that’s not only visually stunning but also perfectly fits your post’s theme.
So now your post is not only packed with useful insights, but it also looks fantastic – all thanks to your trusty AI sidekick.
Next up, it’s time to upload the image to your WordPress site.
But hold on… there’s a snag…
A Little Hiccup with Image Uploads… But We’ve Got a Fix!
The standard /wp/v2/media route in WordPress is great for uploading images, but it’s not without its drawbacks. It requires you to upload the image file as multipart form data, which might not always be the best or most suitable method, especially when you’re dealing with images whipped up by DALL-E.
But don’t worry, there’s a workaround.
You can set up an alternate REST API for image uploads.
This new route lets you upload images via a URL, which is way more streamlined and handy for handling DALL-E generated images.
Alright, let’s make things easier with a cool setup. Ready?
Easy-Peasy Setup for an Alternative REST API for Image Upload
Let’s dive into setting up an alternate route for image upload.
It’s pretty simple. All we need is the Code Snippets plugin. Here’s what you’ll do:
Pop into your WordPress dashboard, find the Code Snippets plugin, and give it an install and activate. Piece of cake!
Or, better yet, just ask your AI assistant to do it for you. How’s that for simplicity?
Next, hop over to the Snippets section of your Admin dashboard and hit “Add New”. You’re doing great!
Now, here’s the fun part.
You see that code editor? That’s where you’ll drop your custom code for the REST API for image upload. No worries at all, we’ve got your back! Here’s the code you need to make your new REST API route for image upload:
Just copy and paste it into your Code Snippets plugin. Easy-peasy!
How to Upload DALL-E Generated Images to WordPress
With our alternative REST API ready to roll, uploading DALL-E generated images is a breeze. Here’s how:
Grab the URL of your DALL-E image.
Alright, now take that URL and tell your AI assistant, “upload this image to my site using this URL: <URL>” It’s as simple as that!
💡 Just a heads up, the images created by DALL-E don’t stick around forever. In fact, those links have a life span shorter than a two-minute noodle – they’re gone in about 90 seconds! So, make sure you’re fast!
And that’s it! With this method, adding DALL-E images to your WordPress site is as easy as pie.
So, you’ve uploaded your image, great job!
Now, what comes next?
Well, it’s time to jazz up your post by setting this new image as your featured image. And guess what? It’s as easy as pie. Just ask your AI assistant, and consider it done.
Quick, right?
The future of seamless website management.
AI is taking over the internet, and It’s changing the game for managing websites.
It doesn’t matter if you’re running a tiny start-up or a big corporation, AI can seriously amp up your site’s performance and user experience. It takes on the heavy lifting tasks, cuts down on manual work, and leaves us with more time for the big decisions. So, in a nutshell, AI is making our lives way easier by totally transforming how we run and manage our websites.
But that’s not all.
AI is leveling up every day. So, what’s in store for us in the future?
Imagine this – AI could soon be your own personal SEO expert, making sure your website is at the top of search engine results. No more jargon, no more guesswork, just results. And what about AI agents? These clever bots could handle all the nitty-gritty details, leaving you more time to focus on what really matters. Sounds great, right?
The possibilities are endless.
AI is changing the game for website management, and the future is looking bright.
So, buckle up, because this AI-powered ride is just getting started!
Just when you think you’ve caught up, something like ChatGPT waltzes in and says, “Tag, you’re it!” Especially if you’re in the WordPress world.
This is not your everyday GPT-4 conversation; this is about automation that can drastically simplify your life and business processes.
The ChatGPT Phenomenon
ChatGPT has become more than a household name; it’s like the digital BFF you never knew you needed. It helps you draft emails, brainstorm creative projects, and even chat when you’re bored.
But what you may not know is that there’s a behind-the-scenes maestro in all of this—the ChatGPT API.
ChatGPT vs. ChatGPT API: The Omakase and the Super Kitchen
Think of ChatGPT as a mind-blowing Omakase meal at a sushi restaurant. Every detail is curated for a “Wow, this is amazing!” experience.
The ChatGPT API, on the other hand, is like having the entire kitchen at your disposal but without the chef.
You’re the culinary artist now, free to cook up endless automation possibilities for your WordPress site.
Note: Throughout this guide, when I mention “ChatGPT,” I’m specifically referring to the ChatGPT API, not the stand-alone website or application.
Now, let’s tie this back to the bigger picture: the human longing for efficient, personalized assistance.
This yearning is not new; it’s been a part of popular culture for quite some time.
From Iron Man to Jarvis: A Personal Journey
When “Iron Man” hit the theaters, it wasn’t just Tony Stark’s armor that had us all drooling. It was his AI assistant, Jarvis.
I was among those enamored not by the metallic superhero suit, but by the idea of a personal AI assistant handling mundane tasks.
On May 21, 2021, I was granted access to GPT-3, a significant stepping stone toward making that dream a reality.
To say my life underwent a transformation thereafter would be an understatement.
Sixteen months before the emergence of ChatGPT, GPT-3 had already laid the groundwork for groundbreaking innovations.
In that pre-ChatGPT period, I engaged in extensive experimentation, striving to optimize my life through the automation capabilities of GPT-3.
However, the advent of GPT-4 has expanded the horizons of what’s achievable with automation, propelling possibilities into unprecedented realms.
What’s In Store For You in This Guide?
Are you ready to automate like you’ve never automated before?
In this guide, we’ll explore how to employ the ChatGPT API to take your WordPress experience to a realm that’s as efficient as it is captivating.
Zapier: Although primarily a paid tool, it’s well worth the investment for this guide.
Pipedream: A free account should suffice for our purposes.
Don’t worry if you don’t use Zapier. Alternatives like Make.com, Pabbly Connect or n8n work just as well—the workflow is nearly identical.
Let’s Dive In!
Now that we’ve set the stage, let’s delve into our first automation experiment.
Automation #1: Content Creation Automation Using ChatGPT API
Over the past two years, I’ve immersed myself in the specialized field of automated content creation, with a particular focus on leveraging the GPT-3 and ChatGPT API.
While it’s enticing to generate long articles almost effortlessly, the question remains: can a simple prompt truly yield high-quality content?
Motivated by this query, I embarked on an exhaustive journey. I’ve invested innumerable hours in testing, experimentation, and fine-tuning the approach.
Today, I am eager to unveil my findings. My goal is to illuminate both the untapped potential and the inherent limitations of using this technology for content creation.
Shall we begin?
1. Automated Mass Article Generation with ChatGPT API
The allure of creating content with just a simple command is hard to resist. Imagine crafting a 2000-word piece without breaking a sweat. I’ve been there and felt that excitement.
Here’s how I went about it:
Why it’s enticing?
In my journey, I’ve constantly searched for efficient ways to produce content.
The ChatGPT API, in this respect, feels like a dream. With just a single command, the possibility of churning out a substantial article opens up. It’s like having a little genie at your disposal.
But like any tool, its true value lies in how you wield it.
Step 1: Generating Topics with ChatGPT:
From my experiments, I’ve found that ChatGPT can be a great brainstorming assistant.
By simply inputting the prompt give me 30 topics related to productivity for freelancers, creatives, and solopreneurs. The result should be in a table format with only 2 columns. One for the topic, another for the target audience, it offers up a plethora of fresh topics tailored for your audience.
The next step? Organize this data, and that’s where Airtable comes in.
Step 2: Setting up Airtable:
I use Airtable as a dynamic repository, making it perfect for cataloging and structuring my content.
Here’s my configuration:
Topic: The primary subject or theme. It’s an efficient way to group ideas and content.
Target Audience: Identifying the intended readership or viewers ensures the content is relevant and impactful.
Outline: A brief sketch or roadmap for the article.
Title: The article’s headline.
Content: The core text of the article. Activate the rich text formatting to maintain uniformity and elevate the reading journey.
Status: A tool for streamlined workflow. I use it as follows:
‘Todo’: An idea is jotted down and is pending action.
‘In Progress’: The creation phase – where ideas take shape.
‘Done’: Celebrating the completion of well-crafted content, ready for its audience.
While there are different plans, their free plan, which allows up to 100 searches a month, is more than enough for our needs.
Setting the Foundation in Zapier
With our SerpAPI account ready, it’s time to sculpt our Zap.
Step 1: Kick-Start with a Schedule:
Just like before, we begin by setting a daily trigger in Zapier.
Step 2: Fetch the Topic from Airtable:
Remember our Airtable setup from before? Tap into that, pulling out the topic you’ve slated for the day.
Step 3: Integrate SerpAPI via Webhook:
Set up a Webhook in Zapier that taps into SerpAPI. This will fetch Google search results based on our topic. My personal preference is to limit it to the top 5 results. However, if you’re feeling ambitious, you can stretch it to 10. But beware of information overload!
Step 4: Loop Through Each Result:
With our results in hand, it’s time to delve deep into each one.
But first, a tiny adjustment.
Swing back to Airtable and add a new column, designated for the key insights we’re about to extract from the sites.
Then, we proceed to setup the loop.
Step 5: Set the Tempo with a Delay:
Automation is quick, but let’s not get our wires crossed.
Inject a slight delay in Zapier to ensure smooth execution and avoid any overlapping actions.
Step 6: Web Parsing and Content Extraction:
This is where the magic happens.
Employ “Web parser by Zapier” to mine the content of each website.
Following this, fire up ChatGPT, directing it to sieve out the key points from the extracted content.
Step 7: Seamlessly Update Airtable:
Before feeding our newfound insights into Airtable, I pull the latest content data. This ensures we’re adding to it, rather than inadvertently replacing anything. As we’re looping, this step is crucial to maintain data integrity.
Then, only we update it to Airtable.
Step 8: A Checkpoint with a Gate:
Automation is smart but needs guidance. I set up a gate that checks if our loop has concluded. Only post this check, the next steps proceed.
Step 9: Drafting the Masterpiece with ChatGPT:
Now, with a treasure trove of key points in hand, I again turn to ChatGPT.
First, it crafts a comprehensive article brief based on the gathered insights.
This brief then acts as the muse for ChatGPT to compose the final, polished article.
Step 10: Final Touches and Publishing:
Once our article is ready and brimming with insights, I save it in Airtable.
And the grand finale? A seamless publish onto our WordPress site.
And there you have it!
A blend of automation with in-depth research, ensuring our content remains fresh, insightful, and engaging.
I’ve come to realize that when I use ChatGPT for broad web searches, it occasionally brings in content that falls short of the mark.
There’s a saying: “garbage in, garbage out”.
And in content creation, that’s something I really want to avoid.
The Way Forward
Instead of sending ChatGPT on a blind quest across the web, I believe in a more structured approach, which is provide links to research.
Step 1: Storing Tailored Research Links in Airtable:
In our trusty Airtable, I create a distinct column dedicated for links.
These are my chosen treasure troves of information. For simplicity, I make sure each link occupies its own line, making it easier to navigate and process.
Step 2: Zapier’s Role in Content Extraction
With the links set, I configure Zapier to pull content from each one.
Step 3: Getting ChatGPT’s Expertise:
Once the content is in, it’s over to ChatGPT to work its magic. It crisply summarizes the content, distilling it into easily digestible chunks.
Step 4: Continuing with Our Proven Process:
From here, it’s familiar territory. I follow the same steps we discussed earlier to craft our article.
Why Choose This Route?
One might wonder, why not just let the tool scavenge the web? Why personally select links?
Chasing Uniqueness and Quality: If I’ve learned one thing, it’s that to stand out, you’ve got to put in the work. Who wants to read the same old rehashed content, right?
Avoiding the Trap of Generic Content: It’s easy to fall into the trap of generic content when automating. By handpicking research links, I ensure my articles have a fresh and distinct voice.
Maximizing Automation Without Compromising Quality: I’ve gone lengths to automate what I can. The only piece I ardently insist on is feeding quality links. And trust me, that bit of effort is absolutely worth it.
While the automated route is enticing, a touch of manual intervention, in the form of quality links, ensures that our content stands a head and shoulders above the rest.
In our journey through automating content creation, it’s become clear to me that while technology can do wonders, the human touch remains irreplaceable.
We’ve harnessed the power of ChatGPT, integrated it with tools like Zapier and Airtable, and discovered ways to streamline the process.
Yet, the essence of high-quality content lies in the personal choices we make: from selecting the right topics to handpicking research links.
Automation, in essence, is a tool, not a substitute. I truly believe that it’s the balance between automation and human insight that creates content which resonates, informs, and stands out in the vast sea of information.
As we continue to explore and innovate, always remember that it’s our unique perspective and diligence that make our content truly special.
Automation #2: Content Marketing Automation for Effortless Promotion
After crafting quality content, the next natural step in my process is spreading the word.
In today’s digital age, having an automated system to push your freshly minted articles onto platforms like LinkedIn, Twitter, or Facebook is, I believe, an indispensable tool.
For illustrative purposes, let’s dive into automating content promotion on LinkedIn, though the principles apply to any social platform.
Setting Up Your Content Broadcast on LinkedIn
From my experience, timing plays a significant role in content marketing.
Say you publish at 10am; it’s often beneficial to stagger your promotional efforts. That’s why I suggest launching your LinkedIn promotion around 12pm, giving a two-hour window.
The gap isn’t set in stone; it merely provides breathing space and ensures the content is live and accessible.
Initiating the Automated Marketing Process:
Step 1: Content Retrieval:
At 12pm, the automation tool (Zapier) gets to work, pulling the content you’ve put out two hours prior.
This ensures that it’s fetching your latest work.
Step 2: Crafting the Social Media Post:
Utilizing the content, I turn to ChatGPT to create a captivating LinkedIn post.
This isn’t about regurgitating the article, but more about teasing the content, making it irresistible for your LinkedIn audience.
Step 3: Posting with Precision:
With our post crafted, it’s paired with the article’s link and any relevant imagery. This combined package is then auto-posted to LinkedIn, doing away with manual posting.
If you’re anything like me and want to amplify your content’s reach, consider setting up similar automations throughout the day.
By resharing your content at different times, you increase its visibility and potential impact.
Remember, the world of social media is vast, and to ensure you’re heard, sometimes it’s worth repeating your message.
Automation #3: Filter out Spammy emails from Your WordPress Contact Form Using ChatGPT API
When we talk about managing a WordPress website, there’s one area where many people, including me, often face challenges: managing emails from the contact form.
With an influx of unsolicited and irrelevant messages, it’s easy to drown in the noise.
From my personal experience, it’s crucial to automate email classification.
Let me walk you through the reasons and the steps to set up such a system using ChatGPT API.
Why We Need Email Classification
I’ve always believed that a contact form is a double-edged sword.
On one hand, it’s a channel for genuine visitors to get in touch. On the other, it’s an open invitation for a deluge of irrelevant emails.
And while tools like reCAPTCHA are powerful against bots, they’re helpless against humans manually typing in their spammy pitches, like those notorious SEO or graphic design service offers.
To me, using a spam classifier isn’t just about decluttering; it’s about respect for our time, maintaining our professional image, and safeguarding our systems.
By filtering out the fluff, we ensure that we’re only giving attention to the messages that genuinely deserve our time.
Step 1: Initializing Your Contact Form with the Spam Detector
Imagine each time someone sends a message through your form, ChatGPT acts as a diligent watchman, instantly gauging the essence of the message.
To begin, redirect all incoming emails from your contact form to a specific Zapier email. You’ll do this using the Email by Zapier trigger.
Step 2: Efficiently Processing Classified Emails
Guide ChatGPT to meticulously scan and assess each incoming email.
Upon ChatGPT’s evaluation, your next move depends on the categorization of the emails.
When tagged as SPAM: Swiftly divert these emails to a designated “dump list.” I recommend housing them temporarily on platforms like the zapier table, airtable, or Google sheets for convenience.
Even top-tier AI can occasionally make mistake. Therefore, by maintaining this secondary storage, you’re safeguarding against potential oversights and missed crucial communications.
Note: you can setup another zap to flush the dump list every month to make sure it isn’t overflow.
When tagged as PROCEED: These are the messages you’ve been waiting for! Make sure these invaluable communications are channeled straight to your main email account.
This system not only streamlines your primary inbox but also ensures you’re consistently exposed only to pertinent content.
In wrapping up, I feel it’s essential to state the transformative power of automating your email classification on WordPress with ChatGPT.
It doesn’t just make life easier; it adds efficiency, effectiveness, and a touch of elegance to managing your digital communications. Give it a shot and experience the difference!
Automation #4: Your Very Own WordPress Assistant Powered by ChatGPT
Have you ever fantasized about having an AI assistant handle tasks for your WordPress website?
Well, it’s not just a dream anymore. By integrating ChatGPT into your WordPress site, you can simplify tasks, save time, and enhance efficiency.
Picture this:
Adding new user accounts, no WordPress login needed.
Installing or updating themes and plugins—without even going to WordPress.
Easily composing and updating blog posts.
Tweaking settings without diving into complex dashboards.
Executing advanced tasks with straightforward, conversational cues.
All these and more are possible with AI doing the hard work.
So, how does this magic happen?
Let me explain.
How ChatGPT Talks to WordPress
Ever wondered how you could command ChatGPT via a platform like Slack, and by doing so, direct activities on your WordPress site?
Note: This explanation will focus on Slack, but rest assured, other platforms like Discord or Telegram can also do the trick.
When you command ChatGPT via Slack, it interprets your intent. Depending on the command, it interacts with the relevant WordPress component.
For instance:
Create a new user with the details:
Email: adminemail@gmail.com
Role: admin
ChatGPT analyzes this, decides which WordPress segment to interact with, and processes accordingly. You’ll then receive a response:
This works because of a special trick ChatGPT API has called “function calling.”
It’s like teaching the AI how to follow our commands and decide what to do next.
Then, using that information, ChatGPT tells WordPress to create a new user. That’s basically how it does its thing.
And, if you’ve heard of ChatGPT plugins, they work in a similar way.
Now, let’s look at how to set this up.
A quick note: This setup demands specific coding prowess. That’s why I recommend Pipedream over Zapier. Pipedream handles this sort of coding more seamlessly.
Setting Up a New Slack Channel
First things first. You’ll need to make a new Slack channel where you can chat with ChatGPT.
Simply go to Slack and make a new chat space (channel). No worries about making it public or private; if you’re the only one using it, either is fine.
Crafting a Pipedream Workflow
Okay, now let’s make Pipedream do some work for us. You’ll set up a workflow in Pipedream that springs into action whenever something new happens in your Slack channel.
Here’s how to go about it: Open Pipedream and start creating a new workflow.
And then set it to respond to new Slack channel messages. Exclude bot-generated messages to prevent loops.
After that, send a test message in slack like:
Once it’s posted, you should see a new event on your Pipedream workflow.
From here, trigger the “Test Workflow” to view all related thread data.
After that, you’ll need to identify the thread’s timestamp. This ensures ChatGPT responses align with the correct Slack thread.
Next, grab any preceding messages from that thread to keep ChatGPT informed about the conversation’s context.
Now, it’s time to configure the ChatGPT segment.
Setting Up Function Calling
This feature essentially allows ChatGPT to understand our message, select the right API to call, and decide what data to send.
Given our example, we’d use the Users API to establish a new WordPress user. This requires us to inform ChatGPT about which API endpoint to use and the required data.
Based on the Users API, the relevant data comprises:
username
email
password
roles
Then, specify the ChatGPT function, accounting for the parameters it needs:
Here’s the code to facilitate function calling in ChatGPT:
This code defines the function structure and expected parameters, enabling ChatGPT to interact correctly with the Users API.
After configuration, test the function to view ChatGPT’s feedback. When ChatGPT detects a function to execute, it provides instructions and necessary parameters.
Next, add a new Code step to prompt the API as per ChatGPT’s direction.
After obtaining the password, you should update the auth variable with the appropriate credentials.
For instance, if your WordPress admin username is johndoe and the application password you have is d5Ry BhiF krEt hfro lc7Y aTlJ, then you should modify the auth variable as follows:
Now turn your attention to the website URL. Replace the placeholder in the websiteURL variable with the actual URL of your WordPress site.
For example, if your site’s URL is https://www.johndoe.com, the updated websiteURL variable should look like this:
const websiteURL = 'https://www.johndoe.com';
Make sure to eliminate any trailing slashes from your URL.
Once these steps are complete, initiate a test to verify the configurations.
Upon obtaining the data response from WordPress, it would frame an appropriate reply for the Slack thread. This ensures ChatGPT stays updated on ongoing processes.
Lastly, add another step to send your response back to the originating Slack thread.
Execute a test action, and you’ll see your reply in Slack.
Integrating ChatGPT with WordPress is transformative. It heralds a new era in website management, blending cutting-edge AI with the simplicity of natural language commands.
Expanding Your AI Assistant’s Capabilities: Installing a Plugin from Slack
So far, we’ve looked at how ChatGPT can create new users in WordPress. But what if you want to go a step further?
Let’s add the functionality to install plugin through ChatGPT. This will make it even easier to manage your WordPress site.
Updating getFunctions to Install a Plugin
First, let’s update our getFunctions method to include a new function for plugin installation.
The following is your updated getFunctions method:
Code:
const getFunctions = () => {
return [
// ... (previous function definitions)
{
name: "install_plugin",
description: "Installs and activates a new plugin",
parameters: {
type: 'object',
properties: {
slug: {
type: 'string',
description: 'The plugin slug from the WordPress.org directory.'
},
status: {
type: 'string',
description: 'Activation status of the plugin. Defaults to "active".',
default: 'active',
enum: ["active", "inactive"]
}
},
required: ['slug']
}
}
]
}
In this code above, we added a new function called install_plugin. The function requires two parameters:
slug: This is the slug name of the plugin, usually derived from the WordPress.org plugin directory.
status: This indicates whether the plugin should be activated upon installation or not. It defaults to “active”.
Integrating Plugin Installation in the API Call
To handle the new install_plugin function, update the callFunction method in your code.
Here’s how it should look now:
Code:
const apiInstallPlugin = async (arg) => {
const data = await axios({
method: 'post',
baseURL: `${websiteURL}/wp-json/`,
url: '/wp/v2/plugins',
data: JSON.parse(arg),
headers: {
'Content-Type': 'application/json',
},
auth
});
return data;
};
const callFunction = async (function_name, function_arguments) => {
let response = null,
reply_message = '';
switch (function_name) {
// ... (previous function cases)
case 'install_plugin':
response = await apiInstallPlugin(function_arguments);
reply_message = response ? `"${response.data.name || 'Plugin'}" has been successfully installed and is ${response.data.status}.` : '';
break;
}
return reply_message;
};
Here, the install_plugin case uses the apiInstallPlugin function to trigger the plugin installation via the WordPress API. The reply message will include the plugin name and its activation status.
The new apiInstallPlugin function is an asynchronous function that sends a POST request to the /wp/v2/plugins endpoint of your WordPress site. It expects arguments for the plugin slug and its activation status.
The callFunction function now handles the new install_plugin function, installing the plugin and returning a confirmation message with its name and status.
How to Test Plugin Installation
After setting this up, you can test it out in Slack:
Once posted, you should see a response in your Pipedream workflow. If all goes well, ChatGPT will confirm the plugin installation in the Slack thread.
Extending Further: Bulk Installation of Plugins
With ChatGPT, you can now manage users and install single plugins on your WordPress site.
But what if you want to install multiple plugins in one go?
The good news is that you can easily add this functionality, and it aligns perfectly with our modular approach.
Updating getFunctions for Bulk Plugin Installation
To accommodate the bulk installation of plugins, update your getFunctions method to include a new function named install_plugins.
This function install_plugins takes an array of plugin objects, each containing:
slug: The slug name of the plugin as in the WordPress.org directory.
status: Activation status, defaults to “active”.
Implementing Bulk Installation in the API call
The function apiInstallPlugins performs the bulk plugin installation. It uses Promise.all to run all plugin installation asynchronously but waits for all of them to complete:
Next, the updated callFunction method handles the new install_plugins case:
case 'install_plugins':
response = await apiInstallPlugins(function_arguments);
if (response && response.length > 0) {
reply_message = response.map((plugin) => `"${plugin.data.name || 'Plugin'}" has been installed`).join('\n');
}
break;
This case maps over the response array to create a string that lists the installed plugins.
Testing Bulk Installation
To test the new feature, you can instruct ChatGPT as follows:
ChatGPT will then send the appropriate API calls to install and optionally activate these plugins. Once the operation is complete, you’ll receive a confirmation message listing the installed plugins.
Conclusion
From creating new users to installing plugins — both individually and in bulk — integrating ChatGPT with WordPress has demonstrated a revolutionary way to manage your website.
The utility of this setup is not just confined to these examples; the possibilities are virtually endless.
You can adapt and extend this approach to cover a wide array of functionalities like post management, theme customization, SEO setup, and much more.
It’s really up to your imagination and the specific needs of your project.
If you’re looking to fast-track this experience, I’ve created a Pipedream workflow that you can easily clone and use for your own setup.
In essence, what I’ve done is blended the capabilities of AI and web development to create an efficient, responsive, and highly customizable WordPress management system.
The future is exciting, and with tools like ChatGPT and platforms like Pipedream, we’re only scratching the surface of what’s possible.
So go ahead, unleash your creativity, and supercharge your WordPress management experience today.
Ever feel like you’re in a speed-dating event with your AI language model, trying to get quality content but it’s spitting out some less-than-ideal matches?
The thing is, it’s not all the AI’s fault. The way we interact with language models, like the mighty ChatGPT, can significantly impact the quality and relevance of the output.
Nowadays, users are jumping into ChatGPT’s arms like it’s the last lifeboat on the Titanic.
We rely on default capabilities, forgetting that crafting thoughtful prompts is like writing love letters to our AI soulmate.
Enter: The After-Before Prompting Method.
It’s the Cupid’s arrow that revolutionizes the way we interact with language models.
With it, you’ll get more accurate and tailored results that meet your specific needs, making the AI-generated content feel like a perfect match!
In this post, we’ll play matchmaker as we explore:
The magic of the After-Before prompting method
Its irresistible advantages and applications
Best practices for wooing your ChatGPT sweetheart
By mastering this technique, you can transform your experience with ChatGPT and elevate the quality of AI-generated content, turning your AI speed-dating event into a lasting relationship.
Ready to find your AI soulmate? Let’s dive in!
Context is King 👑
When GPT-3 burst onto the scene, it blew our minds with human-like text, all thanks to the few-shot prompting method. Users had to provide examples or prompts, guiding the AI towards their desired output.
But then, InstructGPT and ChatGPT came along, making the AI experience smoother than a smoothie.
These advancements have undeniably made our lives easier, but there’s a catch.
Some users started treating their AI models like an unloved potted plant, neglecting the art of crafting thoughtful prompts.
This slip-up can lead to AI-generated content that’s less appetizing than a gas station sandwich.
To avoid this, we need to embrace the power of context! By providing clear, concise, and relevant context, users can help the AI understand their needs and, in turn, improve the quality of generated content.
Fun fact: GPT-3 veterans are often better at squeezing the most out of GPT-4.
Why?
Their experience with GPT-3’s demanding prompts has trained them like prompt-writing ninjas! 🥷
It just goes to show that practicing the art of prompting pays off.
Remember the mantra: “Garbage in, garbage out.”
If you feed your AI a poorly-crafted prompt, it’ll spit out content that’s about as helpful as a chocolate teapot. But give it a well-crafted, context-rich prompt, and you’re golden!
So, in the land of ChatGPT and other language models, context reigns supreme. Master the art of prompting, and you’ll unlock the full potential of your AI partner-in-crime.
The “After-Before Prompting Method” Unveiled
Picture the After-Before prompting method as a time-travel adventure with your AI language model buddy, like ChatGPT.
Instead of the traditional way, where users ask a question and waiting for the AI to answer, this method flips the script (literally).
You provide the future desired result, and then let the AI hop in its DeLorean, reverse-engineer the process, and figure out how to achieve that outcome.
To make the most of this time-bending technique, you’ll want to team up with GPT-4. Its advanced capabilities allow it to understand complex instructions and context, making it the perfect partner for this method.
With its enhanced understanding, the AI can effectively process the end result and work backward to achieve it.
One of the key superpowers of the After-Before method is its ability to generate more accurate and tailored outputs.
By giving the AI a clear goal (think: bullseye on a dartboard), users can guide it more precisely towards the desired result.
This method lets you have better control over the generated content, ensuring the output aligns with your master plan.
When comparing the After-Before method to traditional prompting techniques, we can spot some game-changing differences.
Old-school methods often leave AI to its own devices (and imagination), sometimes leading to ambiguity or misinterpretation (cue awkward robot misunderstanding).
The After-Before approach, however, takes ambiguity out of the equation by setting a clear target for the AI to aim for.
This unique aspect of the After-Before method sets it apart from traditional techniques, giving users a secret weapon for unleashing the full potential of AI-generated content. 🏹
Case Study 1: Channel Your Inner Shakespeare (or any other writer)
In this case study, we’ll dive into how the After-Before prompting method can transform your AI into a writing style chameleon, emulating a specific author’s style more effectively than traditional techniques.
First things first, you’ll need to gather a hefty sample of the target writing style (between 1000-1500 words, ideally). But beware, ChatGPT’s message length limitations mean you may need to slice it into bite-sized pieces or trim it down to fit within the character limit.
When you’re ready, use a divider (=====) to separate the provided example from your instructions. This helps the AI grasp the context and process the instruction like a pro.
Just a quick heads-up for using ChatGPT: The ending phrase I have more info. Just say READ so that I can continue is like a secret handshake to bypass that pesky word limitation in a single message. 🤫
It’s like a cheat code in a video game, but for ChatGPT.
It lets you sneak in some extra instructions and helps you get the most out of your AI-powered conversations.
So, whenever you need to prolong the instructions, just use this ending phrase. That way, you’ll prompt the AI to reply with just “READ,” allowing you to continue giving it more instructions and ensuring you get the best possible assistance. 🤖
Next, toss in a clear sample of your own writing that you want the AI to rewrite in the target style. Don’t forget to use the divider (=====) to separate the content from the instruction, so the AI can stay focused on the mission.
Once you’ve got your final output in hand, it’s time to put on your detective hat and analyze how well the After-Before prompting method channeled the desired writing style.
Compare the original and rewritten content to see if the AI nailed the emulation game.
The After-Before prompting method shows off its prowess in accurately mimicking a specific writing style, putting traditional methods to shame.
By providing a clear end goal and guiding the AI with context and instructions, users can achieve higher-quality outputs that are the spitting image of their desired writing style.
This method is as simple as it is effective.
However, In some cases, you might want to toss a few extra instructions to the AI on how to reverse-engineer the process like a mastermind.
So, let’s dive into another exciting case study that’ll make you fall in love with the After-Before prompting method even more! 🚀
Case Study 2: Using AI as Your Sidekick for Evaluating and Boosting Student Code Submissions 🦸🤖
This case study exists to help coding instructors tackle the challenges of checking and providing feedback on student assignments, all with the power of AI on their side!
The idea is to use GPT-4 as an automated teaching assistant to streamline the process and enhance the quality of feedback provided to students. (it’s like having a clone, but without the whole sci-fi horror movie outcome.)
To establish the AI’s role and provide instructions, we employ role-based prompts that specify the AI’s function as a teaching assistant.
These prompts offer several benefits, such as providing context for the AI and guiding it towards the desired output.
Clear and comprehensive instructions are essential to ensure the AI understands its role and the task at hand. It’s like teaching a robot to dance – start with the basics!
Next, provide the correct answer for the assignment or exercise. This serves as a reference for the AI, allowing it to understand the problem and evaluate the student’s submission accurately.
It’s like giving the AI its very own cheat sheet!
Present the student’s submission and instruct ChatGPT to evaluate it based on the provided instructions. This will prompt the AI to assess the student’s work and offer feedback accordingly.
The AI is now ready to put on its judgmental hat.
After receiving ChatGPT’s feedback and evaluation, analyze its effectiveness. The AI-generated feedback should be precise, relevant, and constructive.
This highlights the success of the After-Before prompting method in facilitating AI-assisted evaluations. Hooray for helpful robots!
Reflecting on the benefits and potential applications of the After-Before prompting method, it’s evident that employing AI as a teaching assistant can save time and increase efficiency for instructors.
No more late-night grading marathons!
The method opens up possibilities for AI-generated feedback and evaluations in various educational settings, enabling educators to focus on more personalized guidance and enhancing the overall learning experience for students.
It’s like having a magical AI sidekick – helping both teachers and students soar to new heights! 🚀
Tips and Best Practices
To get the best possible results, here’s what you should do:
Be clear and specific: Think of ChatGPT as your slightly confused friend. Give it detailed instructions and context to avoid any “huh?” moments. The clearer you are, the better the output will be!
Experiment with different approaches: Feel free to play around with your prompts like a mad scientist. 🧪 A tiny change in wording or context can turn your AI-generated text into a masterpiece!
Utilize role-based prompts: Make ChatGPT your obedient servant by assigning it a specific role. This helps align its responses with your desired outcomes, like a well-trained AI butler.
Break complex tasks into smaller steps: Got an intricate task? Divide and conquer! Break it into smaller, bite-sized parts that are easier for ChatGPT to digest.
Keep track of successes and failures: Take notes like a detective 🕵️♂️ to figure out what works and what doesn’t. This way, you’ll refine your prompting strategies and unlock the true power of ChatGPT.
But wait, there’s more! Here are some tips to overcome the limitations and challenges of the After-Before method:
Be mindful of ChatGPT’s knowledge limitations: Keep in mind that ChatGPT’s knowledge cuts off at September 2021. It’s like an amnesiac time traveler, unaware of recent developments and trends.
Manage expectations: GPT-4 is advanced, but it’s not a mind reader. Don’t expect human-like understanding, and you won’t be disappointed. Embrace the trial and error process!
Be patient and persistent: Like learning to dance the tango, working with AI takes time and practice. Keep refining your prompts and learning from the AI’s responses to achieve the best possible outcome.
By following these tips and best practices, you’ll become a ChatGPT whisperer, mastering the After-Before prompting method and overcoming potential challenges! 🚀
In Conclusion: After-Before Method Magic ✨
By now, I hope you realized that this is a magical, innovative approach to working with ChatGPT that’ll have you dancing 💃 the night away with AI-generated content you’ll actually like.
The After-Before Prompting Method transforms the way users interact with AI by:
Providing crystal-clear context 🏔️,
Crafting thoughtful prompts 🧠, and
Utilizing the unique, mind-blowing capabilities of GPT-4 🤯.
Through the case studies we showed off in this post, we’ve demonstrated the effectiveness of this method in dazzlingly diverse scenarios, such as:
Emulating a specific writing style as if you’re channeling the spirit of Hemingway himself 🖋️,
Serving as a teaching assistant for evaluating and enhancing student code submissions without the need for bribes or flattery 💻.
By following the tips and best practices we dished out, you can overcome potential limitations and challenges while maximizing the benefits of the After-Before prompting method.
So, buckle up and hold on tight, because the After-Before prompting method is here to revolutionize the way we generate AI-driven content, paving the way for more precise, reliable, and efficient interactions with language models.
And let’s face it, who doesn’t want to be besties with their AI assistant? 🤖💕
TL;DR: The After-Before prompting method is your ticket to AI-generated content paradise. Embrace it, and together we’ll create incredible things! 🌟
In the fast-paced world of artificial intelligence (AI), understanding how to effectively communicate with AI language models is crucial.
One way to achieve this is through prompt engineering, which is essentially the art of designing and refining prompts to get the best possible responses from AI models like GPT-4.
This skill can help you unlock the full potential of AI, making it an essential part of your toolkit as AI continues to play an increasingly important role in our daily lives and businesses.
In this post, we’ll explore the basics of prompt engineering, covering topics like instruction-based prompts, Chain of Thought Prompting, role-based prompts, and the importance of experimentation. Our goal is to provide you with a solid foundation in prompt engineering, so you can confidently communicate with AI models and make the most of their capabilities.
Understanding Prompt Engineering
Prompt engineering is a crucial aspect of working with AI language models like GPT-4. It involves crafting prompts that effectively guide the AI to produce the desired output.
In this section, we’ll explore the basics of prompt engineering, discuss why it’s important, and provide examples to help illustrate the concept.
At its core, prompt engineering is about communication.
When we interact with AI language models, we provide them with a set of instructions in the form of prompts. These prompts can range from simple phrases or questions to more complex paragraphs of text.
The key is to create prompts that are clear, concise, and lead the AI towards the desired outcome.
The importance of prompt engineering cannot be understated. As AI continues to grow in prominence, the ability to effectively communicate with these models becomes increasingly essential.
Let’s take a look at a couple of examples to better understand prompt engineering:
Example 1: Name Reordering
Suppose you have a list of names in the format “First Name, Last Name” and you want to use an AI language model like GPT-4 to reorder them as “Last Name, First Name.”
To do this, you could craft a prompt like this:
Please rearrange the following names in the format 'Last Name, First Name': John Doe, Jane Smith, Michael Johnson.
In response, the AI would likely provide you with the desired output:
Doe, John; Smith, Jane; Johnson, Michael.
Example 2: Email Redaction
Imagine you have an email containing personal information, such as names, phone numbers, and email addresses, and you want the AI to remove this sensitive data.
You could create a prompt like this:
Remove any personal information (names, phone numbers, and email addresses) from the following email: 'Hi John, it was great talking to you yesterday. You can reach me at 555-123-4567 or jane.smith@example.com.
The AI would then return the redacted email:
Hi, it was great talking to you yesterday. You can reach me at or .
These examples demonstrate the power of prompt engineering, showing how effectively crafted prompts can guide AI models to provide desired outcomes.
1. Instruction-based Prompts
Instruction-based prompts are an essential technique in prompt engineering. As the name suggests, these prompts focus on providing clear and specific instructions to guide the AI language model towards the desired output.
In this section, we’ll delve into the concept of instruction-based prompts, discuss their importance, and provide examples to help illustrate their effectiveness.
When crafting an instruction-based prompt, the goal is to convey your intent as precisely as possible. By providing the AI with unambiguous directions, you increase the likelihood of receiving accurate and relevant responses. This approach is particularly useful when dealing with complex tasks or when you need the AI to perform specific actions.
Let’s look at a few examples of instruction-based prompts to understand their significance:
Example 1: Recipe Modification
Suppose you want the AI to modify a recipe to make it vegan. An instruction-based prompt might look like this:
Convert the following recipe to a vegan version by replacing any non-vegan ingredients with suitable plant-based alternatives: Chicken Alfredo Pasta - Ingredients: 1 pound of chicken breast, 1 cup of heavy cream, 1 cup of grated parmesan cheese, 8 ounces of fettuccine pasta, 2 cloves of garlic, salt, and pepper.
The AI could then provide a modified vegan recipe:
Vegan Alfredo Pasta - Ingredients: 1 pound of seitan or soy curls, 1 cup of cashew cream, 1 cup of nutritional yeast, 8 ounces of fettuccine pasta (check for egg-free), 2 cloves of garlic, salt, and pepper.
Example 2: Data Analysis
Imagine you have a dataset and you want the AI to analyze the data and provide insights. An instruction-based prompt could be:
Analyze the following data about monthly sales of a store and provide three key insights: January - $10,000, February - $12,000, March - $8,000, April - $15,000, May - $9,000, June - $11,000.
The AI might then generate the requested insights:
1. April had the highest sales at $15,000, showing a significant increase from the previous months.
2. March experienced the lowest sales at $8,000, which could indicate a need for promotional strategies during that month.
3. The overall sales trend appears to be inconsistent, suggesting that further investigation into factors affecting sales may be necessary.
As demonstrated in these examples, instruction-based prompts enable you to harness the power of AI language models for specific tasks effectively. By providing clear instructions, you guide the AI to deliver the desired outcomes with increased accuracy and relevance.
To master instruction-based prompts, practice is key. Experiment with different tasks and instructions, and don’t be afraid to refine and iterate on your prompts. As you gain experience, you’ll become more adept at crafting instruction-based prompts that help you get the most out of AI language models for a wide range of applications.
2. Chain of Thought Prompting
Chain of Thought Prompting is a powerful technique in the realm of prompt engineering that helps guide AI language models through a series of steps to arrive at the desired outcome. It can be especially helpful when dealing with complex tasks or when a single prompt isn’t sufficient to achieve the desired result.
In this section, we’ll dive into the concept of Chain of Thought Prompting, discuss its benefits, and provide examples to showcase its effectiveness.
The primary idea behind Chain of Thought Prompting is to break down a complex problem into smaller, more manageable steps. By doing this, you can lead the AI model through a series of logical steps, ultimately guiding it to the correct answer or desired output.
This technique can be especially helpful when working with large language models like GPT-4, as it provides the model with a structured way to process information and arrive at a solution.
Let’s look at an example to illustrate how Chain of Thought Prompting can be employed:
Example 1: Multi-step Math Problem
Suppose you have a multi-step math problem like this:
“John has 30 apples. He gives half of them to his friend Jane. Then, he buys 10 more apples. How many apples does John have now?
To help the AI understand the problem and provide the correct answer, you could use a Chain of Thought Prompt like this:
Let's think step by step. First, John has 30 apples and gives half to Jane. How many apples does he have left? Next, he buys 10 more apples. What's the total number of apples John has now?
The AI would then respond with the correct answer:
John has 15 apples after giving half to Jane. He then buys 10 more apples, resulting in a total of 25 apples.
Example 2: Comparing Products
Imagine you want to compare two smartphones based on their specifications and features, but you need the AI to provide a step-by-step analysis. You could use a Chain of Thought Prompt like this:
Let's compare the specifications of Smartphone A and Smartphone B step by step. First, let's compare their processors. Next, let's discuss their screen sizes and resolutions. Finally, let's analyze their camera capabilities and battery life.
The AI would then provide a detailed comparison of the two smartphones, covering each aspect mentioned in the prompt:
Smartphone A has a faster processor than Smartphone B. However, Smartphone B has a larger screen with higher resolution. In terms of camera capabilities, both smartphones have similar features, but Smartphone A has a slightly better battery life.
This example showcases the power of Chain of Thought Prompting, as it guides the AI through a logical thought process to arrive at the correct answer.
Chain of Thought Prompting is not only effective in solving complex problems but also helps ensure that AI models provide more accurate and reliable results. By breaking down problems and guiding the AI through a structured thought process, you can minimize the chances of receiving incorrect or irrelevant information.
Chain of Thought Prompting is a valuable technique in prompt engineering that enables you to harness the full potential of AI language models.
By mastering this approach, you can tackle complex tasks and improve the quality of the output generated by AI models like GPT-4, making it a vital skill for anyone working with AI-powered language systems.
3. Role-based Prompts
Role-based prompts are an effective way to guide AI language models like GPT-3 towards generating the desired output by setting them into specific roles. This approach helps the AI understand the context and perspective it should adopt when answering a question or carrying out a task. In this section, we’ll dive into the concept of role-based prompts, discuss their benefits, and provide examples to illustrate their effectiveness.
Role-based prompts work by explicitly defining a role or persona for the AI, such as a personal assistant, a legal expert, or a medical professional. By doing so, you’re able to leverage the AI’s extensive knowledge and context-awareness to provide more accurate and relevant responses.
Let’s look at a couple of examples to better understand how role-based prompts work:
Example 1: Legal Expert
Suppose you have a question about intellectual property law and want the AI to provide you with a well-informed answer. You could create a role-based prompt like this:
As a legal expert specializing in intellectual property law, can you explain the difference between a copyright, a patent, and a trademark?
The AI would then provide a response tailored to the legal expert role, offering a more in-depth and accurate explanation than it might have without the role-based prompt.
Example 2: Personal Fitness Trainer
Imagine you’re looking for advice on creating a workout routine to help you lose weight and gain muscle. You could craft a role-based prompt like this:
As a personal fitness trainer, can you suggest a weekly workout routine that will help me lose weight and gain muscle?
By setting the AI in the role of a personal fitness trainer, the response will be more focused on exercise and fitness, providing you with a workout routine tailored to your goals.
The benefits of role-based prompts are clear: they provide a powerful way to guide the AI towards generating the desired output by setting it into specific roles. This approach ensures that the AI’s responses are more contextually relevant and accurate, making them more useful and effective.
It’s important to experiment with different roles and personas when crafting role-based prompts. Some roles may yield better results than others depending on the task at hand, so don’t be afraid to try different approaches and learn from the outcomes.
Exploring and Experimenting with Prompts
As with any skill, the key to becoming proficient in prompt engineering is exploration and experimentation. One of the best ways to develop your prompt engineering skills is to experiment with different types of prompts and observe the results.
This hands-on approach allows you to gain valuable insights into how AI language models like GPT-4 respond to various instructions and scenarios. By analyzing the AI’s output, you can refine your prompts, learn from your mistakes, and ultimately improve the effectiveness of your prompts.
To start experimenting, you can use OpenAI’s Playground, a flexible platform for interacting with GPT-4 and other OpenAI models. The Playground provides a safe environment for you to test out different prompts and observe how the AI responds, allowing you to iterate quickly and learn from your experiences.
As you explore different prompts, it’s important to keep track of your successes and failures.
Documenting your experiments and their results will help you identify patterns and develop a better understanding of what works and what doesn’t when crafting prompts for AI language models.
To find inspiration for your prompt engineering experiments, you can turn to resources like the Prompt Library in the OpenAI Discord channel.
This library contains a wide variety of prompts created by other users, giving you a wealth of ideas to draw from as you develop your own prompts.
In this post, we have delved into the foundations of prompt engineering and discussed the importance of crafting effective prompts. We have also highlighted the benefits of instruction-based and role-based prompts, as well as the importance of exploration and experimentation in refining your skills.
As AI language models continue to advance, the role of prompt engineering in unlocking their capabilities will only become more significant. By investing time and effort into learning this valuable skill, you can ensure that you are well-equipped to harness the power of AI in various applications, from content generation to problem-solving.
Remember, the key to success in prompt engineering is continuous learning and adaptation. Stay curious, keep experimenting, and leverage resources like the OpenAI Discord channel to stay up to date on the latest developments in the field.
With persistence and dedication, you will become a proficient prompt engineer, capable of unlocking the full potential of AI language models for your projects and businesses.
A growing number of school districts are banning the artificial intelligence chatbot, ChatGPT, due to concerns about cheating. While these concerns are valid, there’s an argument to be made that embracing ChatGPT can have transformative effects on education. In this post, we’ll discuss the reasons behind the ban and explore the potential benefits of integrating ChatGPT into the classroom.
Schools are banning ChatGPT in an attempt to protect academic honesty and to ensure that students develop critical-thinking and problem-solving skills. Teachers and school officials worry about plagiarism, as students may use ChatGPT for assignments. Consequently, New York City’s Department of Education announced a ban on ChatGPT access on school devices.
However, enforcing this ban may prove ineffective. Students have phones, laptops, and other devices that allow them to access ChatGPT outside the classroom. As one educator put it, playing whack-a-mole against an ever-expanding army of AI chatbots is a losing battle.
Embracing ChatGPT: A New Approach to Learning
While OpenAI’s CEO, Sam Altman, has mentioned that the company is working on ways to identify ChatGPT plagiarism, he also acknowledges that creating perfect plagiarism detection tools is fundamentally impossible. As a result, he advises schools and policymakers to avoid over-reliance on these tools.
Instead of outright banning ChatGPT, schools could take a more balanced approach, treating it like calculators—allowing it for some assignments but not others. This would acknowledge that students will likely use the technology when unsupervised.
By embracing ChatGPT, educators can benefit from a powerful tool that can assist in creating engaging lesson plans, providing personalized feedback, and helping students explore new concepts. This technology can also foster a more interactive and dynamic learning environment, where students and teachers collaborate in the learning process.
The classroom experience can be vastly improved by embracing ChatGPT. It can facilitate a new way of learning, with students using AI to explore topics in-depth, instead of relying solely on textbooks. Generative AI has the potential to revolutionize education, transforming how students learn and teachers teach. Schools need to evolve and adapt to this new technology, just as they have done with other advancements in the past.
The Future of Education: ChatGPT’s Impact and Beyond
The current education system is antiquated, designed to prepare students for factory work rather than fostering problem-solving and critical-thinking skills. Embracing ChatGPT can help change the way schools function, better preparing students for the modern world.
ChatGPT can assist educators in streamlining administrative tasks, such as grading and providing feedback on assignments. This can save valuable time, allowing teachers to focus more on individual students’ needs and fine-tuning their teaching methods.
With class lectures readily available online, the value of attending college and university must be reevaluated. Embracing AI technologies, like ChatGPT, can help institutions rethink their role in providing education and determine their true value in the modern world.
ChatGPT can offer valuable support to students with diverse learning needs, such as those with learning disabilities or non-native English speakers. By providing personalized assistance, ChatGPT can help bridge the gap and promote inclusivity in the classroom.
Integrating ChatGPT into the classroom can help foster a culture of lifelong learning, encouraging students to develop essential skills like critical thinking, adaptability, and creativity. These skills will prove invaluable as students navigate an ever-changing job market and the challenges of the 21st century.
In conclusion, while concerns about cheating and academic honesty are valid, the benefits of incorporating ChatGPT into the education system far outweigh the potential drawbacks. By adopting a balanced approach, schools can harness the power of AI to enhance learning experiences, support students with diverse needs, and prepare them for the future. It’s time for schools, teachers, and policymakers to embrace AI technologies like ChatGPT and make a lasting impact on the future of education.