The Power of Large Language Models

I just posted a new video explaining large language models in plain English 👇

If you like having written notes to skim or reference later, I put together a simple breakdown below. Consider the following your cheat-sheet version to the video (but don’t rob yourself of the video…that’s the real experience!).

At the simplest level, a large language model (LLM) is just AI that predicts the next word in a sentence based on what came before.

That sounds almost too easy… but the crazy part is how far that simple idea scales.

I like to think of it like this: imagine you had a friend who has read basically the entire internet — books, Wikipedia, Reddit, articles, code, everything — and can instantly reply to any question you text them.

That’s basically what models like ChatGPT, Gemini, and Claude are doing.

The “large” part isn’t marketing hype. It’s literal.

These models are trained on hundreds of billions to trillions of words and contain billions (sometimes trillions) of parameters, which are adjustable numbers that shape how the model predicts language.

So yeah… they’re big in both data and brainpower.

Computers don’t understand words — they understand numbers.

So when you type text into an LLM, it first gets broken into pieces called tokens (words or parts of words).
Then each token gets converted into numbers and mapped into something called an embedding — basically a coordinate in a giant “meaning space.”

Words with similar meanings end up close together. Totally different words end up far apart.

So the model isn’t seeing text anymore — it’s seeing patterns in math.

Modern LLMs use a neural network design called a transformer, introduced in the famous 2017 paper Attention Is All You Need.

The key idea is attention — the model looks at how every word relates to every other word in a sentence at the same time.

So instead of reading word-by-word like old AI systems, it understands context across the whole sentence (or paragraph).
That’s what makes responses feel coherent and human-like.

When an LLM writes text, it’s not pulling sentences from memory.

It’s predicting probabilities for the next token and sampling from them.
That’s why you don’t always get the same answer twice.

During training, the model learned these probabilities by making predictions, measuring error, and adjusting its parameters billions of times: a process called gradient descent.

So while large language models can feel almost magical when you use them, underneath it all, they’re built on a surprisingly simple idea: predicting what comes next.

What makes them powerful isn’t a single trick. It’s scale. Massive data, massive models, and an architecture designed to understand context across language.

The result is a system that can take human text, turn it into math, find patterns across billions of examples, and generate new language that feels natural to us.

And that’s why tools powered by large language models suddenly feel like they can write, explain, code, and reason — because in a very real sense, they’ve learned the statistical structure of human language itself.