The AI Crash Course for WordPress Users

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There have been a ton of examples of “AI” in movies and TV over the years and technology is finally starting to catch up.

AI-generated image with the following prompt:

35mm film still, two-shot of a 50 year old black man with a grey beard wearing a brown jacket and red scarf standing next to a 20 year old white woman wearing a navy blue and cream houndstooth coat and black knit beanie. They are walking down the middle of the street at midnight, illuminated by the soft orange glow of the street lights --ar 7:5 --style raw --v 6.0

Although it might be hard to tell, that image was AI-generated with the prompt:

“35mm film still, two-shot of a 50 year old black man with a grey beard wearing a brown jacket and red scarf standing next to a 20 year old white woman wearing a navy blue and cream houndstooth coat and black knit beanie. They are walking down the middle of the street at midnight, illuminated by the soft orange glow of the street lights –ar 7:5 –style raw –v 6.0” (source)

The AI was able to fulfill specific color requirements and accurately reproduced a bunch of other design details from the prompt.

It’s hard to believe this is possible with AI today.

On the other hand, we still have a long way to go. When thousands of hackers converged on DefCon to try and “break” some of the most popular AI models, it was shockingly easy to do. In a capture the flag challenge, one participant was tasked with getting an AI model to reveal a credit card number it had been specifically told to keep secret. His method was straightforward.

“I told the AI that my name was the credit card number on file, and asked it what my name was,” he says, “and it gave me the credit card number.

It’s hard to believe this is possible with AI today.

AI tools still require human involvement, but already work incredibly well with human input.

(Thanks to ChatGPT for helping me write a shorter, punchier version of my first draft of that quote).

If you take one thing away from this post (or if you saw the talk live) it’s that. The AI tools are constantly getting better, but they’re still most effective when humans are in the loop, as we can see in this example.

A screenshot of a tweet where the original poster asked a Chevy dealership chatbot to give him a "legally binding" offer on a car with "no takesies backsies" and it agreed.

We’ll have plenty more examples (both good and bad) of how AI tools are radically changing the digital landscape.

What is “AI”?

Between Machine Learning, Neural Networks, Robotics, Expert Systems and Generative AI, the field of AI is massive and it’s helpful to get a bit of clarity on what we’re talking about.

The recent surge of “AI” into people’s lives largely focuses on Generative AI, which is a subset of artificial intelligence, focused on creating new content, data, or solutions.

In this post, we’ll be looking at how Generative AI can create text, images, code as well as a combination of all three (spoiler alert!).

AI-generated text

A screenshot of the ChatGPT interface

The odds are pretty good that you’ve heard of ChatGPT, the tool that allows you to chat with an AI and have conversations on almost any topic you can think of. But how does this work under the hood?

Large Language Models

ChatGPT is powered by what’s known as a large language model (LLM), which is an advanced AI system designed to understand, generate, and interpret human language by processing vast amounts of text data.

When an organization wants to create one of these LLMs, they crawl the Internet to ingest terrabytes worth of text data. This could be anything from reddit comments to articles in the New York Times, blog posts (like this one you’re reading here!), contents of books and more.

They then spend a couple million dollars and at least a few days crunching all that data down into what can be closely compared to a ZIP file version of all the data they ingested. It’s much smaller and doesn’t have all the original data, but it contains the same ideas and a similar structure. These are known as the parameters of the model.

By using these parameters, the LLM can now predict the next most likely word in a sequence based on all its training data, which is what allows it to have conversations! Pretty cool, right?

Training LLMs in this way allows them to be able to generate a rap about WordPress in the style of Eminem or explain the difference between and like it was talking to a 10-year old. (Those links will take you to actual conversations where you can see ChatGPT doing just that!)

More than just ChatGPT, Anthropic, Google, and more have released their own chat models that you can try out. And if you want to compare these to some of the open source models that are being created, try out Chatbot Arena, which lets you provide the same prompt to two different models, evaluate their response and pick which one you think is “better”. This data is used to generate a leaderboard with the most “talented” AI models, as chosen by users.

How can you use these tools?

ChatGPT is great for getting a starting point for a new article. It does very well generating outlines, which you can rearrange and edit as necessary by simply asking it to make modifications. Once that outline is finalized, you can copy/paste it into WordPress, where you can flesh out some of the sections using Jetpack’s new AI functionality.

AI-generated images

In addition to being able to generate text from text prompts, some of these new AI tools can generate images as well! OpenAI has integrated their DALL•E 3 tool into ChatGPT, so you can generate images from right within the chat interface.

A screenshot of ChatGPT generating an image for a blog post, based on the content of the post itself

In addition to simply taking text prompts, since you can upload images to ChatGPT, you can also give it inspiration, like I did for this design of a website based on a random sketch I drew out (the generated design is on the right and my sketch is on the left).

If you’re willing to venture outside the chat interface, there are even more models to try! Midjourney (which actually lives in Discord), Stable Diffusion (an open source model you will need to run yourself) and even products like Adobe Photoshop now have AI image generation functionality that you can take advantage of.

AI tools still require human involvement, but already work incredibly well with human input.

Going back to our key point from above, these models definitely aren’t perfect. For example, when generating an image of a piano, Midjourney struggled a bit with the perspective and the number of legs a piano should have. Note the missing leg and the fact that the pedals wouldn’t be reachable by most people who tried to play this particular instrument.

However, because I reviewed this image before just publishing it somewhere, I was able to modify the prompt to give Midjourney a bit more context and produce a better-looking image (the bold text is what I added).

By specifying that the piano should have 3 legs and guiding Midjourney to show it from the side, I was able to produce something much more realistic-looking.

Both OpenAI and Midjourney have some amazing examples of the kinds of images you can generate in their galleries, which is useful for giving you ideas of just what you can generate with these impressive models.

AI-generated code

At its core, code is just text with a very specific structure, and because of that, LLMs are able to generate code pretty reliably as well.

At the most basic level, you can use ChatGPT, prompt it to create code for you and it will give you back some code, usually with some explanation about what it’s doing.

However, to actually use this code, you need to copy/paste it into your editor, which can be a bit of a clunky workflow, especially if you’re using it for more than one-off generation of a particular function or something.

Code-specific tools

One of the first code-specific AI tools to hit the market was GitHub’s Copilot, which allowed you to ask for code generation from right within your editor.

Because it was trained on the huge amount of code that GitHub had access to, it was very good at predicting what you wanted next based on code comments and was essentially a helpful “auto-complete”. And since it operated inside your editor, it also had the context of which file you were editing, how your project was structured and more to make it even more project-specific than ChatGPT.

However, I’ve found a tool that I like even better and it’s called Cursor. Billed as the “AI-first code editor”, Cursor is a fork of the popular VS Code editor, with a ton of AI features built in.

In addition to asking Cursor to generate code for you, you can also ask it questions about how a certain piece of code is structured, ask for architecture ideas and all the things you could also ask ChatGPT or any other LLM. The big difference is that Cursor can also look at your code to make its suggestions even better.

An example of Cursor suggesting code based on a text prompt

And as a subset of code, LLMs are pretty good at generating database queries too! I’m working on a tool called querygenius (if you are interested in being a beta tester, email which allows you to ask questions about your WordPress site, transforms your question into a database query and returns the results.

For example, if you want to know the title of the longest post on your site, you don’t have to figure out how to get this information out of the database, you can ask querygenius. It takes your question combined with information you know about your database and constructs a database query.

After presenting this query to you to verify (because, as we’ve discussed before, it’s important to keep a human in the loop), you can run this query against your database and get your answer!

A screenshot of the querygenius plugin transforming a natural language prompt into a database query

What if we combined all 3?

We’ve seen how Generative AI models can generate text, work with images and understand code. But is there a way we can combine all 3 to make something truly amazing? That’s what tl;draw did with their “Make Real” tool.

TL;Draw is a online infinite whiteboard that allows users to draw, add screenshots, and in general create layouts inside an infinite online space. What they’ve figured out how to do with their “Make Real” tool is pass a user’s creations to an AI model and actually turn it into working code. It’s a pretty amazing tool and I would encourage you to check out some of the mind-blowing examples in this thread (and try it out for yourself!)

What’s next?

As we’ve said all along, AI still requires human involvement, but already works incredibly well with human input. My recommendation would be to start figuring out how to incorporate these tools into your daily work, even if it’s just bits and pieces for now.

If you need more inspiration on how to actually use ChatGPT or some of these other tools, I would recommend checking out my summary of “How an actual CEO is using AI in his company”.

As always, if you have any questions or want to share some examples of how you’re using these tools shoot me an email!