This Is How LLMs Work — Spoiler: They Don’t Think

Let’s clear something up right away: Large Language Models (LLMs) don’t “think.” They don’t understand your questions. They’re not sitting there contemplating answers like some digital Einstein.

What they do is guess. Over and over, at speed and scale.

But before we get to the mechanics of how LLMs like ChatGPT, Claude, or Gemini generate human-like text, let’s take two steps back.

You can’t talk about LLMs if you don’t get AI and machine learning basics first. Because these models didn’t drop out of the sky—they were built, trained, and tuned.

First, What Is AI?

AI, broadly, is about building machines that can mimic human tasks. Not feelings. Not consciousness. Tasks. Like spotting patterns, translating languages, recognising faces.

Think of AI like a calculator. You give it rules and inputs, and it gives you something useful—only now, those “rules” are learned from data, not hardcoded by a developer.

This leads us to…

Machine Learning: Teaching Machines Without Telling Them Everything

Machine learning (ML) is a subset of AI. Here, we don’t program specific answers. We feed data into a system and let it find patterns. The more data, the better it gets.

For example, show a machine thousands of photos labelled “cat” or “not cat” and it starts figuring out—on its own—what makes a cat a cat. Curved ears, certain textures, whiskers. It doesn’t know what a cat is. It just knows what patterns show up when humans say “cat.”

LLMs work the same way. But with language.

Now, Enter the LLM

A Large Language Model is a machine trained to predict the next word in a sentence. That’s the job. That’s all it does.

You type: “Cybersecurity teams are struggling to…”

It might guess: “keep up.”

Why? Because in millions of documents, those words often follow that setup.

It didn’t understand the sentence. It didn’t read the news. It just recognised a pattern in data and guessed what comes next.

Do that billions of times, and you get something that sounds… smart.

Training an LLM: All Prediction, No Comprehension

To build an LLM, you:

1. Gather billions of words from books, websites, code bases.
2. Break the text into tokens (chunks of words or letters).
3. Feed it into a neural network.
4. Task the model with predicting the next token—again and again.
5. Adjust internal weights (math knobs) every time it gets it wrong.
6. Repeat this until the guesses get scarily good.

But here’s the twist: it never understands anything. It’s just very, very good at guessing the next token based on the last ones.

Architecture? Transformers. Think of It Like Attention Span for Machines.

LLMs use something called a Transformer model. Without going deep into the weeds, just know this: it lets the model “pay attention” to all the words in your input, not just the last one. That’s why it can respond coherently to a full paragraph, not just the final sentence.

It’s like having a conversation partner who remembers everything you said—instantly, and with perfect recall.

Fine-Tuning and Safety Nets

Once you have a model that spits out halfway coherent language, you still need to train it to be useful. So, teams fine-tune it:

• With specific datasets (customer service logs, medical notes, code samples).
• With human feedback (like thumbs up/down on answers).
• With rules that say “Don’t give advice on illegal stuff” or “Don’t be offensive.”

Still, it’s not foolproof. Models hallucinate. They spit out confident lies. They can be biased. Again: no understanding, just pattern replication.

So Why Does It Feel So Smart?

Because language feels like thinking. If someone responds smoothly, we assume comprehension. But in the case of an LLM, it’s just rapid-fire statistics dressed up as conversation.

It’s like autocomplete on steroids—with a personality.

Your Takeaway: LLMs Don’t Think. But They Predict So Well, It Feels Like They Might.

They’re not conscious. Not sentient. Not secretly alive. They’re machines trained to recognise linguistic patterns and play them back in ways that mimic intelligence.

When they’re helpful, it’s because the training data aligned with your needs. When they’re wrong, it’s because no one taught them that bit—or they hallucinated a guess.

Use them like tools. Powerful, sharp, but blind to meaning.

That’s the real magic and the real risk.