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Category: ChatGPT

27 min read ChatGPT, AI Summarizer, Claude, Gemini, Prompt Engineering

How to Use AI to Quickly Digest Long-Form Content Like a Pro

We live in an age of information overload.

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.

  1. Larger Context Windows:
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    • 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.
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    • 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!
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  2. Improved Recall Capabilities:
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    • 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.
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    • Context Retention: This improved memory means that important themes and details are retained, leading to coherent and thorough summaries.
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  3. Depth and Detail in Summarization:
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    • 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.
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    • 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:

  1. Surface-Level Summaries:
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    • Such prompts tend to produce summaries that only scratch the surface, missing deeper insights.
    • Key examples, strategies, and important details are often omitted.
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  2. Lack of Depth:
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    • 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.

Seth Godin’s episode on the Tim Ferriss Show was our guinea pig.

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 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 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 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.

Happy summarizing!

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21 min read ChatGPT

Building an AI Assistant For Your WordPress site Using Custom GPT

Imagine you’re the captain of a ship, steering your business through the digital ocean.

Your WordPress site is the vessel, and it’s loaded with precious cargo – your content, products, and brand. But there’s a catch: you’re swamped with tasks, like a sailor trying to navigate through a storm with an old map. Managing a WordPress site for your business can feel like battling the high seas. You’re constantly installing plugins, updating content, and trying to stay afloat in the competitive waters. It’s overwhelming, like trying to sail a ship single-handedly.

But what if I told you there’s a secret first mate, ready to join your crew?

The captain is feeling overwhelmed trying to sail the ship single-handedly.
The captain is now relaxed and happy, given that his AI assistant is handling all the heavy lifting for him.

This isn’t just any crew member.

It’s a powerful ally that can turn the tide in your favor, streamlining your journey and guiding your ship to the harbor of success. In this guide, we’ll unveil how a Custom GPT AI assistant can revolutionize the way you manage your WordPress site. It’s like having an expert navigator on board, one who understands the winds of digital marketing and the currents of content creation.

Prepare to embark on a journey that will transform your WordPress site management from a daunting task into a smooth-sailing adventure.

Meet Your WordPress Site’s New BFF, Custom GPT AI


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.

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:

{
  "openapi": "3.1.0",
  "info": {
    "title": "AI Assistant for Your WordPress site",
    "description": "AI assistant for WordPress websites, capable of performing tasks via the WordPress REST API.",
    "version": "v0.0.1"
  },
  "servers": [
    {
      "url": "https://www.your_website_url_here.com/"
    }
  ],
  "paths": {
    "/wp-json/wp/v2/posts": {
      "get": {
        "summary": "List all posts",
        "operationId": "getPosts",
        "parameters": [
          {
            "name": "context",
            "in": "query",
            "description": "Scope under which the request is made; determines fields present in response.",
            "required": false,
            "schema": {
              "type": "string",
              "enum": [
                "view",
                "embed",
                "edit"
              ],
              "default": "view"
            }
          },
          {
            "name": "page",
            "in": "query",
            "description": "Current page of the collection.",
            "required": false,
            "schema": {
              "type": "integer",
              "default": 1
            }
          },
          {
            "name": "per_page",
            "in": "query",
            "description": "Maximum number of items to be returned in result set.",
            "required": false,
            "schema": {
              "type": "integer",
              "default": 10
            }
          },
          {
            "name": "search",
            "in": "query",
            "description": "Limit results to those matching a string.",
            "required": false,
            "schema": {
              "type": "string"
            }
          }
        ]
      },
      "post": {
        "summary": "Create a post",
        "operationId": "createPost",
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": {
                "type": "object",
                "properties": {
                  "title": {
                    "type": "string",
                    "description": "Title of the post"
                  },
                  "content": {
                    "type": "string",
                    "description": "Content of the post"
                  },
                  "author": {
                    "type": "integer",
                    "description": "ID of the author"
                  },
                  "status": {
                    "type": "string",
                    "description": "Status of the post",
                    "enum": [
                      "publish",
                      "future",
                      "draft",
                      "pending",
                      "private"
                    ]
                  },
                  "featured_media": {
                    "type": "integer",
                    "description": "ID of the featured media"
                  }
                },
                "required": [
                  "title",
                  "content"
                ]
              }
            }
          }
        }
      }
    },
    "/wp-json/wp/v2/posts/{id}": {
      "get": {
        "summary": "Retrieve a specific post",
        "operationId": "getPostById",
        "parameters": [
          {
            "name": "id",
            "in": "path",
            "required": true,
            "description": "Unique ID of the post",
            "schema": {
              "type": "integer"
            }
          }
        ]
      },
      "post": {
        "summary": "Update a specific post",
        "operationId": "updatePost",
        "parameters": [
          {
            "name": "id",
            "in": "path",
            "required": true,
            "description": "Unique identifier for the post.",
            "schema": {
              "type": "integer"
            }
          }
        ],
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": {
                "type": "object",
                "properties": {
                  "title": {
                    "type": "string"
                  },
                  "content": {
                    "type": "string"
                  },
                  "author": {
                    "type": "integer"
                  },
                  "status": {
                    "type": "string"
                  },
                  "featured_media": {
                    "type": "integer"
                  }
                }
              }
            }
          }
        }
      },
      "delete": {
        "summary": "Delete a specific post",
        "operationId": "deletePost",
        "parameters": [
          {
            "name": "id",
            "in": "path",
            "required": true,
            "description": "Unique identifier for the post.",
            "schema": {
              "type": "integer"
            }
          },
          {
            "name": "force",
            "in": "query",
            "description": "Whether to bypass trash and force deletion.",
            "required": false,
            "schema": {
              "type": "boolean",
              "default": false
            }
          }
        ]
      }
    },
    "/wp-json/wp/v2/media-from-url": {
      "post": {
        "summary": "Upload an image",
        "operationId": "uploadImage",
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": {
                "type": "object",
                "properties": {
                  "url": {
                    "type": "string"
                  },
                  "title": {
                    "type": "string"
                  },
                  "alt": {
                    "type": "string"
                  },
                  "caption": {
                    "type": "string"
                  }
                }
              }
            }
          }
        },
        "responses": {
          "201": {
            "description": "Media uploaded successfully"
          },
          "400": {
            "description": "Bad request"
          }
        },
        "security": [
          {
            "apiKey": []
          }
        ]
      }
    },
    "/wp-json/wp/v2/plugins": {
      "get": {
        "summary": "Retrieve a list of plugins",
        "operationId": "getPlugins",
        "responses": {
          "200": {
            "description": "Successful response"
          }
        },
        "parameters": [
          {
            "name": "context",
            "in": "query",
            "required": false,
            "schema": {
              "type": "string"
            }
          },
          {
            "name": "search",
            "in": "query",
            "required": false,
            "schema": {
              "type": "string"
            }
          },
          {
            "name": "status",
            "in": "query",
            "required": false,
            "schema": {
              "type": "string"
            }
          }
        ]
      },
      "post": {
        "summary": "Install a plugin",
        "operationId": "installPlugin",
        "responses": {
          "201": {
            "description": "Plugin created successfully"
          }
        },
        "parameters": [
          {
            "name": "slug",
            "in": "query",
            "required": true,
            "schema": {
              "type": "string"
            }
          },
          {
            "name": "status",
            "in": "query",
            "required": false,
            "schema": {
              "type": "string"
            }
          }
        ]
      }
    },
    "/wp-json/wp/v2/plugins/{plugin}": {
      "get": {
        "summary": "Retrieve a specific plugin",
        "operationId": "getPlugin",
        "responses": {
          "200": {
            "description": "Successful response"
          }
        },
        "parameters": [
          {
            "name": "context",
            "in": "query",
            "required": false,
            "schema": {
              "type": "string"
            }
          },
          {
            "name": "plugin",
            "in": "path",
            "required": true,
            "schema": {
              "type": "string"
            }
          }
        ]
      },
      "post": {
        "summary": "Update a plugin",
        "operationId": "updatePlugin",
        "responses": {
          "200": {
            "description": "Plugin updated successfully"
          }
        },
        "parameters": [
          {
            "name": "context",
            "in": "query",
            "required": false,
            "schema": {
              "type": "string"
            }
          },
          {
            "name": "status",
            "in": "query",
            "required": false,
            "schema": {
              "type": "string"
            }
          },
          {
            "name": "plugin",
            "in": "path",
            "required": true,
            "schema": {
              "type": "string"
            }
          }
        ]
      },
      "delete": {
        "summary": "Delete a plugin",
        "operationId": "deletePlugin",
        "responses": {
          "200": {
            "description": "Plugin deleted successfully"
          }
        },
        "parameters": [
          {
            "name": "context",
            "in": "query",
            "required": false,
            "schema": {
              "type": "string"
            }
          },
          {
            "name": "plugin",
            "in": "path",
            "required": true,
            "schema": {
              "type": "string"
            }
          }
        ]
      }
    }
  },
  "components": {
    "schemas": {}
  }
}

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:

Install the following plugins on my site:
Contact Form 7
Code Snippets
Enable SVG, WebP & ICO Upload  

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:

/**
 * Register a new REST API endpoint that allows user to upload images into the media library using url
 * 
 */

add_action( 'rest_api_init', 'wpgpt_register_rest_images_endpoint' );

function wpgpt_register_rest_images_endpoint() {
    register_rest_route( 'wp/v2', '/media-from-url', array(
        'methods' => 'POST',
        'callback' => 'wpgpt_rest_upload_image_from_url',
        'permission_callback' => function () {
            return current_user_can( 'upload_files' );
        }
    ) );
}

function wpgpt_rest_upload_image_from_url( $request ) {
    $url = $request->get_param( 'url' );
    $title = $request->get_param( 'title' );
    $alt = $request->get_param( 'alt' );
    $caption = $request->get_param( 'caption' );
    
    $attachment_id = wpgpt_rest_upload_image_from_url_to_media_library( $url, $title, $alt, $caption );

    if ( is_wp_error( $attachment_id ) ) {
        return $attachment_id;
    }

    $attachment = wp_prepare_attachment_for_js( $attachment_id );

    return rest_ensure_response( $attachment );

}

function wpgpt_rest_upload_image_from_url_to_media_library( $url, $title = null, $alt = null, $caption = null ) {
    require_once( ABSPATH . 'wp-admin/includes/image.php' );
    require_once( ABSPATH . 'wp-admin/includes/file.php' );
    require_once( ABSPATH . 'wp-admin/includes/media.php' );

    $tmp = download_url( $url );

    $file_array = array(
        'name' => sanitize_title( $title ) . '.png',
        'tmp_name' => $tmp
    );

    $id = media_handle_sideload( $file_array, 0, $title );

    if ( is_wp_error( $id ) ) {
        @unlink( $file_array['tmp_name'] );
        return $id;
    }

    if ( ! empty( $alt ) ) {
        update_post_meta( $id, '_wp_attachment_image_alt', $alt );
    }

    if ( ! empty( $caption ) ) {
        update_post_meta( $id, '_wp_attachment_image_caption', $caption );
    }

    return $id;
}

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:

  1. 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!

28 min read ChatGPT, GPT-4, WordPress Automation

Taking WordPress to Infinity and Beyond with ChatGPT API

Ah, the digital revolution.

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.

Tools You’ll Need

To follow along, you’ll require a few tools:

  • OpenAI API Key: Here’s where you can get it.
  • 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.

For a practical illustration, visit this Airtable example.

Step 3: Zapier Automation:

Efficiency thrives on automation. This is where Zapier, my trusted sidekick, steps in to make things seamless:

1. Setting the Stage:

To kick things off, I establish a daily routine in Zapier, defining precisely when the magic should commence.

2. Scouting for Topics in Airtable:

Zapier then leverages its Airtable ‘find record’ action. Its mission? To comb through Airtable and pinpoint a topic marked ‘Todo’.

3. Drafting with ChatGPT:

With the selected topic, we turn to our digital maestro, ChatGPT. Here, it takes on two pivotal roles:

3.1 Crafting a Brief:

Using the title and target audience details, ChatGPT sketches an outline, setting the groundwork for the article.

3.2 Building the Narrative:

Armed with the outline, ChatGPT then meticulously crafts the full article, giving life to the idea.

4. Organizing the Outcome:

Once sculpted to perfection, the article finds its place in Airtable—organized, categorized, and awaiting its debut.

5. Publication Prelude:

The grand finale? Employing the WordPress ‘create post’ action, the article gracefully takes the stage, inviting the world to revel in its content.

To sum it up, it’s a harmonious ballet of ChatGPT, Airtable, and Zapier, synchronized perfectly to the rhythm of streamlined content creation.

Want to replicate this efficiency in your workflow? Get the full Zap template here: https://go.nathanonn.com/zap-wp-content-creation

2. Enhancing Article Quality with a Research Tool

The world of automated content creation is mesmerizing, isn’t it? But like any tool, it has its drawbacks.

Let me walk you through some challenges I’ve encountered and how I’ve worked around them.

The Problem with Solely Depending on ChatGPT

When I first began using ChatGPT, the novelty was exhilarating.

But over time, I noticed a trend. The articles, though coherent and informative, had a certain… sameness to them.

As more enthusiasts jumped on the ChatGPT bandwagon, the internet began flooding with eerily similar content.

For readers, this repetition became a bore, making them drift away.

That’s when I realized: depending solely on ChatGPT meant compromising on originality.

Incorporating a Research Phase

In my pursuit of authenticity, I realized the value of good old-fashioned research.

Just because I was automating the content creation didn’t mean I had to forgo the depth that comes from thorough study.

The strategy is simple but effective:

  • Research Google based on the article’s title. This would gave us a broader perspective on what’s already out there.
  • Next, summarize the crux of the top search results. This step added layers to the content, making it richer and more informative.
  • From these summaries, we use ChatGPT to fish out the five most captivating tidbits related to the topic. These became the pillars of our article.
  • With this foundation, we direct ChatGPT to draft an article outline, which then blossomed into the full content.

Using SerpAPI:

My secret weapon in this journey? SerpAPI. It allowed me direct access to Google’s search results. With it, I could effortlessly:

  • Fetch the top 5 results related to the topic. This ensured I was capturing the most relevant content.
  • Dive into these articles to distill the essence. The goal? To condense the key insights from these pieces.
  • The best part? I didn’t have to do this manually. With the help of Zapier, these tasks were automated, further streamlining my workflow.

if you copy from one author, it’s plagiarism, but if you copy from many, it’s research.
Wilson Mizner

Step-by-Step Zapier Setup for Research Integration

Let’s dive deep into the nitty-gritty of the setup. Trust me, it’s simpler than it sounds!

Getting Started with SerpAPI

To commence our research, SerpAPI is our tool of choice. So, first things first, sign up for a free account on SerpAPI and secure your API key.

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.

Want to replicate this Zapier workflow? Get the full Zap template here: https://go.nathanonn.com/wp-content-creation-research

But, is that all?

3. Providing Directed Research for Better Content

The Issue with Open-ended Web Hunts

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.

Want to replicate this Zapier workflow? Get the full Zap template here: https://go.nathanonn.com/content-creation-directed-research

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.

Want to replicate this Zapier workflow? Get the full Zap template here: https://go.nathanonn.com/wp-content-marketing


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:

import OpenAI from 'openai';

const callOpenAIAPI = async (openai, messages = [], functions = [], function_call = 'auto') => {
    const chatCompletion = await openai.chat.completions.create({
        model: "gpt-3.5-turbo-16k",
        messages: [...messages ],
        functions,
        function_call,
        temperature: 0
    });
    return chatCompletion;
}

const getFunctions = () => {
  return [
    {
      name: "create_user",
      description: "Creates a new user",
      parameters: {
        type: 'object',
        properties: {
          username: {
            type: 'string',
            description: 'The username of the user (based on the email address)'
          },
          email: {
            type: 'string',
            description: 'The email address of the user'
          },
          password: {
            type: 'string',
            description: 'Automatically generated random password containing 12 characters (uppercase, lowercase, numbers and symbols)'
          },
          roles: {
            type: 'array',
            description: 'The roles of the user. EX: ["administrator"], ["subscriber"]',
            items: {
              type: "string",
              enum: ["administrator", "author", "editor", "subscriber"]
            }
          },
        },
        required: [ 'username', 'email', 'password', 'roles' ]
      }
    }
  ]
}

export default defineComponent({
  props: {
    openai: {
      type: "app",
      app: "openai",
    }
  },
  async run({steps, $}) {
    const openai = new OpenAI({
      apiKey: this.openai.$auth.api_key
    });
    const newMessage = steps.trigger.event.text;
    const messages = steps.previous_messages.$return_value;
    const functions = getFunctions();

    messages.push({
      role: 'user',
      content: newMessage.trim()
    });

    return await callOpenAIAPI( openai, messages, functions );
  },
})

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.

Here’s the code needed:

import axios from 'axios';

const websiteURL = '__your_website_url__';
const auth = {
    username: '__your_admin_username__',
    password: '__enter_your_application_password__'
};

const apiCreateUser = async (arg) => {
    const data = await axios({
        method: 'post',
        baseURL: `${websiteURL}/wp-json/`,
        url: '/wp/v2/users',
        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) {
        case 'create_user':
          response = await apiCreateUser(function_arguments);
          reply_message = response ? `User #${response.data.id} (${response.data.email}) has been created.` : '';
          break;
    }
    return reply_message;
};

export default defineComponent({
    async run({ steps, $ }) {
      let reply_message = '';
      const response_from_chatgpt = steps.process_user_message_with_chatgpt.$return_value.choices[0].message;
      const is_function_calling = response_from_chatgpt && response_from_chatgpt.function_call && response_from_chatgpt.function_call.name && response_from_chatgpt.function_call.name.length >= 0 ? true : false;

      if ( is_function_calling ) {
        reply_message = await callFunction(response_from_chatgpt.function_call.name, response_from_chatgpt.function_call.arguments);
      } else {
        reply_message = response_from_chatgpt && response_from_chatgpt.content ? response_from_chatgpt.content.trim() : '';
      }

      return reply_message;
    },
  })

This segment instructs Pipedream on how to act based on ChatGPT’s feedback, especially for creating new users on WordPress.

For this, you’ll need a WordPress application password. Learn how to generate one here: https://www.paidmembershipspro.com/create-application-password-wordpress/

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:

const auth = {
    username: 'johndoe',
    password: 'd5RyBhiFkrEthfrolc7YaTlJ'
};

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.

Here’s the updated part:

Code:

{
    name: "install_plugins",
    description: "Installs multiple plugins at once",
    parameters: {
      type: 'object',
      properties: {
        plugins: {
            type: 'array',
            description: 'List of plugins to install',
            items: {
                type: 'object',
                properties: {
                    slug: {
                        type: 'string',
                        description: 'WordPress.org plugin directory slug.'
                    },
                    status: {
                        type: 'string',
                        description: 'The plugin activation status.',
                        default: 'active',
                        enum: ["active", "inactive"]
                    },
                },
                required: ['slug']
            }
        }
      },
      required: ['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:

const apiInstallPlugins = async (arg) => {
    const { plugins = [] } = JSON.parse(arg);
    const data = await Promise.all(plugins.map(async (plugin) => {
            const response = await apiInstallPlugin(JSON.stringify(plugin));
            return response;
        }
    ));
    return data;
};

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.

Get started right away by clicking on this link: Clone Pipedream Workflow.

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.

10 min read ChatGPT

The After-Before Prompting Method: The Secret Sauce to Getting the Most Out of ChatGPT

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.

The content’s original source: MM: We’re shutting down Daily CMO podcast…

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:

  1. 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!
  2. 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!
  3. 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.
  4. 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.
  5. 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:

  1. 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.
  2. 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!
  3. 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:

  1. Providing crystal-clear context 🏔️,
  2. Crafting thoughtful prompts 🧠, and
  3. 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! 🌟

12 min read ChatGPT, GPT-4

Prompt Engineering 101: Mastering the Art of Crafting AI Prompts

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.

Join the Discord channel here: https://discord.com/invite/openai.

Conclusion

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.

4 min read ChatGPT

Why Schools Should Embrace ChatGPT: A Game-Changer for Education

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.