A short explanatory video by Matt Wolfe that describes, at a high level, what occurs after a user submits a prompt to ChatGPT: tokenization of text, neural-network inference across billions of parameters predicting the next token, and the role of GPUs and data centers in performing massive parallel computations to produce a response.
Chapters
When you type a prompt into ChatGPT, it might seem like you're talking to some kind of robot in your computer, but that's not what's actually happening. You're actually triggering a huge chain reaction of mind blowing complexity. So let me break it down. When you type a prompt into the chat box, it gets instantly converted into a string of numbers called tokens. These tokens are how the model understands our language. It doesn't ever read actual human language. That's why you get the classic Strawberry problem. But here's the part that makes LLMs one of the most complex inventions in modern day history. Your prompt then flows through billions of artificial neurons inside a giant neural network trained on a massive chunk of the Internet. So how does it figure out what to say back? Well, it doesn't look up answers like Google. Instead, it predicts the most likely next word, one token at a time, based on the patterns it's learned during its training. It's like a super powered autocomplete, but with memory, reasoning and context. And that prediction process requires serious computing power. Your prompt gets sent to a data center filled with GPUs, which are like the muscle behind AI. These GPUs run all of the models calculations at the same time, analyzing trillions of data points to generate the best response. The result is an output that feels like your LLM is thinking in real time, but in reality, it's the result of millions of math equations happening in seconds. So next time you use ChatGPT, remember you're not just chatting with a robot. You're tapping into one of the most advanced pieces of AI infrastructure humans have ever built.