What is an LLM (large language model) and how it works inside

Every time you write to Claude or ChatGPT, you’re talking to an LLM. The acronym sounds technical, but the idea underneath is simpler than it seems. An LLM (large language model) is the engine that makes AI understand what you ask and reply with words that make sense.

In this blog I explain what an LLM is and how it works inside, without formulas and without jargon. When you understand how the machine “thinks,” you use it better and trust it in the right measure.

What an LLM is in one sentence

An LLM is a program that learned to predict the next word from reading enormous amounts of text. That’s all it is, and at the same time it’s a whole lot.

Think about your phone’s keyboard, the one that suggests the next word as you type. An LLM is the giant, far smarter version of that. Instead of having seen your messages, it read an immense portion of what humanity has written: books, articles, forums, documentation, conversations. From so much reading, it learned the patterns of language: which word tends to follow another, how an explanation is built, how a useful answer sounds.

How it learns: patterns, not memory

Here’s the part that surprises most people. The LLM does not store the texts it read the way you store files in a folder. There’s no drawer with a ready-made answer waiting for you to look it up.

What it does is different: while it “trains,” it adjusts millions (sometimes billions) of internal values called parameters, until it becomes extremely good at a single game: guessing which word comes next. A word in a sentence is hidden, it tries to guess it, it compares itself to the real one and corrects. It repeats that trillions of times. In the end, those patterns are etched into its structure, not as saved sentences, but as a statistical intuition of how language works.

That’s why an LLM can write you a poem that never existed or solve a problem no one wrote before: it isn’t copying it, it’s building it word by word following what it learned.

How it answers: one word at a time

When you ask a question, the LLM doesn’t think up the whole answer at once. It builds it piece by piece.

1. It reads your message and turns it into numbers (tokens, which are chunks of words).
2. It calculates the most likely word that should come next, based on everything it learned.
3. It writes that word, then starts again: now with your message plus that new word, it calculates the next one. And so on, until the answer is complete.

It’s like a narrator telling a story word by word, choosing each one based on what it already said and what you asked for. It happens so fast it feels instant, but inside it’s that chain of predictions.

The key: an LLM doesn’t “know” things the way a person knows them. It predicts text that sounds right. Most of the time it’s correct, and that’s why it’s so useful.

Why it sometimes gets things wrong (or makes things up)

Understanding this saves you trouble. Because the LLM predicts what sounds good, it sometimes produces something that sounds perfect but is false. That’s called a hallucination: the AI invents a fact, a quote or a link with full confidence, because statistically it “fit,” even if it isn’t real.

It doesn’t do it to lie. It does it because its job is to complete convincing text, not to verify facts. That’s why:

• Always verify the important data (dates, numbers, names, laws, prices).
• Give it context: the clearer and more complete your prompt, the better the answer.
• Use it for drafts and organization, and you provide the final judgment.

Why it’s worth knowing this

When you understand that the LLM is a word predictor trained on a huge amount of text, you stop expecting magic and start getting real value out of it. You give it clearer instructions, you paste in the information it needs, and you review what it hands back. That’s where AI truly makes you more efficient.

If you want to keep bringing these concepts down to earth, you’ll enjoy understanding what machine learning is and also what RAG is and how it makes AI more reliable.

You don’t need to be an engineer to use an LLM well. You need to understand what it does and what it doesn’t. Start small: the next time you write to an AI, remember you’re guiding a very smart predictor, and you’ll see how your results improve.