How ChatGPT Works, Explained Simply [Written by ChatGPT]
This is my previous blog post rewritten by OpenAI’s ChatGPT. The original post.
ChatGPT isn’t what most people think of as “AI.” It’s not a sentient machine or a human-like thinker. Instead, it’s a supercharged word-guessing engine—a glorified “What Comes Next” machine.
When you give it some words, it calculates what the next word is most likely to be, then the word after that, and so on until it predicts the end of the sentence. But how does it do this? Let’s break it down step by step—simple, oversimplified even—but enough to give you the gist of how this technology works and what it can (and can’t) do.
A Tiny Bit of Math (I Promise, It’s Friendly)
Wait, don’t run away—this will be painless!
If you’ve taken a statistics class, you may recall regression analysis. That’s the equation y = mx + b. In plain terms, for a given value of x, the equation predicts the value of y.
Now, imagine expanding that idea to lots of inputs: y = mx₁ + nx₂ + … + px₁billion + b.
This is the basic idea behind how ChatGPT predicts the next word: It takes all the words (inputs), runs them through a fancy equation, and calculates the most likely next word.
(Here’s a visual—just one, I promise!)
It’s Not About Words, It’s About Tokens
Okay, I lied a little. ChatGPT isn’t a word predictor—it’s a token predictor.
What’s a token? Think of it as the building block of language. When you type in a sentence, ChatGPT doesn’t see it as a collection of words but as tokens.
For example:
“What’s in a name? That which we call a rose by any other name would smell as sweet.”
This sentence might break down into tokens like this:
[What] [’s] [ in] [ a] [ name] [?] [ That] [ which] [ we] [ call] [ a] [ rose] [ by] [ any] [ other] [ name] [ would] [ smell] [ as] [ sweet] [.]
Each token is then converted into a number. Those numbers are plugged into an ultra-complex version of that regression equation we mentioned earlier. Out pops the next most probable token, which is then converted back into a word (or punctuation).
How Does It Know What to Predict?
The Training Process
The magic lies in training.
To train ChatGPT, its creators fed it an enormous amount of text. Think public domain books, Wikipedia articles, blog posts, Reddit threads, forum discussions—basically, anything freely (and sometimes not-so-freely) available online.
This data is used to fine-tune the equation, or algorithm, that powers ChatGPT.
“But algorithms have been around forever,” you might say. “What’s the big deal?”
Here’s the twist: No one—not even the creators—knows exactly what this algorithm looks like. It’s the result of millions (or billions) of adjustments during training, too complex to summarize in a simple formula.
And that’s part of what feels “intelligent” about it, though the philosophical debate about whether it truly is intelligent can wait for another day.
What ChatGPT Can’t Do
While ChatGPT is remarkably capable, it’s not perfect. It doesn’t “understand” like humans do. It doesn’t have intentions, emotions, or a conscience—it’s just crunching numbers and guessing words based on patterns in its training data.
It’s great for generating text, answering questions, or sparking ideas, but it’s not infallible. Sometimes, its guesses are wrong or make no sense because it lacks real-world reasoning.
In Conclusion
This explanation is intentionally oversimplified, and technically savvy readers might spot inaccuracies. But my goal was to make this technology understandable for the average high school graduate with little or no technical background.
Hopefully, I succeeded. Now you can sound smart at your next dinner party! 😊