How LLMs Work — The Plain English Version

You already know how this works (sort of)

Have you ever started typing a text message and your phone suggested the next word? That’s the basic idea behind large language models (LLMs). They predict what word comes next.

That’s it. That’s the core concept.

ChatGPT, Claude, Gemini — these AI tools that seem almost magical? At their heart, they’re doing something conceptually simple: reading what you wrote, and predicting what should come next. They’re just incredibly good at it.

How do they get so good?

Imagine you read every book ever written. Every Wikipedia article. Every Reddit post. Every news story. Billions and billions of words.

After reading all of that, you’d probably be pretty good at predicting what comes next in a sentence, right? If someone said “The cat sat on the ___,” you’d say “mat” or “couch” without thinking.

That’s essentially what happened with LLMs. They were trained on enormous amounts of text from the internet. Not by memorizing it — but by learning patterns. Patterns in how words relate to each other, how sentences are structured, how ideas connect.

The training process (in human terms)

Think of it like this:

1. Read a LOT. The model processes billions of pages of text.
2. Play a guessing game. For every piece of text, it tries to guess the next word. It gets most guesses wrong at first.
3. Learn from mistakes. When it guesses wrong, it adjusts slightly. Over billions of guesses, it gets better and better.
4. Get feedback from humans. Real people rate the model’s responses, helping it learn what’s helpful vs. unhelpful.

After this process (which takes weeks on thousands of specialized computers), the model has learned patterns that let it write essays, answer questions, write code, translate languages, and more — all by predicting the next word, over and over.

Why they seem so smart

Here’s the thing that surprises people: LLMs don’t “understand” things the way you do. They don’t have beliefs or experiences. But the patterns they’ve learned are so rich and complex that their outputs look like understanding.

When you ask an LLM to explain quantum physics, it’s not “thinking about” quantum physics. It’s generating text that follows the patterns of how quantum physics is explained in the training data it learned from.

This is why LLMs can:

• ✅ Write fluently and naturally
• ✅ Summarize complex topics
• ✅ Answer a huge range of questions
• ✅ Write code, poetry, emails, and more

But also why they sometimes:

• ❌ Make up facts that sound convincing (“hallucinations”)
• ❌ Get tripped up by simple logic puzzles
• ❌ Give different answers to the same question

The “large” in large language model

Why does size matter? Two reasons:

1. More parameters = more patterns. A bigger model can learn more subtle and complex patterns. It’s like the difference between someone who read 10 books vs. 10 million books.
2. Emergent abilities. At certain sizes, models suddenly gain abilities they weren’t explicitly trained for — like being able to do math or reason through problems. Researchers are still figuring out why this happens.

What this means for you

You don’t need to understand the math to use LLMs effectively. But knowing these basics helps you:

• Set realistic expectations. LLMs are powerful pattern matchers, not all-knowing oracles.
• Understand hallucinations. When an LLM makes something up, it’s because the pattern “felt right” even though the fact was wrong.
• Write better prompts. The more context and pattern you give it, the better its predictions will be.

The bottom line

LLMs are sophisticated next-word predictors trained on vast amounts of text. They learn patterns, not facts. They generate text, they don’t “think.” And despite those limitations, they’re remarkably useful tools that are changing how we work, create, and learn.

That’s the essential version. Want to go deeper? Check out the 🔵 Applied version to learn how to use this knowledge practically, or the 🟣 Technical version if you want to peek under the hood.