How Do LLMs Actually Work?
Over the past few years, AI has become part of almost everything we do. It writes code, answers questions, summarizes articles, helps with homework, and even chats with us like a real person.
But have you ever wondered how it actually works?
The funny thing is...
It's much simpler than most people think.
It all starts with predicting the next word
At its core, a Large Language Model (LLM) does one very simple job.
It looks at the text you've already written and asks:
"What's the most likely next token?"
That's it.
Imagine you type:
"Today the weather is..."
Most people would probably think of words like:
- nice
- sunny
- cold
- rainy
An LLM does exactly the same thing—but instead of relying on intuition, it relies on patterns it learned from an enormous amount of text.
Once it predicts one token, it repeats the process again.
"Today the weather is beautiful..."
Now it predicts the next one.
"Today the weather is beautiful because..."
Then the next.
And the next.
This happens dozens of times every second until an entire paragraph appears.
So... how does it know what to write?
Because it has been trained on an enormous collection of text.
Books.
Articles.
Documentation.
Forums.
Research papers.
Websites.
The exact training data varies from one company to another, and modern models are typically trained on carefully curated datasets rather than simply "the entire internet."
The important part is this:
The model isn't trying to memorize everything.
Instead, it learns the relationships between words.
For example:
- "cat" often appears near "meow."
- "JavaScript" frequently appears with words like "function," "const," or "async."
- The phrase "The capital of France" is very often followed by "Paris."
After seeing billions of examples, the model begins to understand how language is structured.
It doesn't actually see words
Humans think in words.
Computers think in numbers.
Before an LLM can process your text, it breaks it into small pieces called tokens.
For example:
Hello, world!
might become something like:
["Hello", ",", " world", "!"]
Or sometimes a single word is split into multiple tokens.
Each token is then converted into a number.
Internally, the model isn't reading text anymore.
It's processing numbers.
The magic of embeddings
A token isn't represented by just one number.
Instead, it's transformed into a long list of numbers called an embedding.
You can think of an embedding as a mathematical description of a word's meaning.
Because of embeddings, the model learns that:
- "cat" and "dog" are somewhat similar.
- "car" and "automobile" are extremely close.
- "Python" could refer to either a programming language or a snake, depending on the surrounding context.
Words become points in a huge mathematical space where similar meanings naturally end up close together.
The secret ingredient: Attention
Here's where things get really interesting.
Not every word in a sentence is equally important.
Consider this sentence:
John gave the book to Alex because he had already finished reading it.
Who does "he" refer to?
John?
Alex?
Humans solve this by looking at the surrounding context.
LLMs do something very similar using a mechanism called Attention.
Every time the model generates a new token, it looks back at all the previous tokens.
Some are more relevant than others.
The model learns which ones deserve more attention.
This idea is the foundation of the Transformer architecture, the breakthrough that powers modern language models like ChatGPT.
What do billions of parameters actually mean?
You'll often hear people say things like:
"This model has 70 billion parameters."
Parameters are simply the numbers the model learns during training.
At the beginning, they're almost random.
During training, they're adjusted billions of times until the model becomes better at predicting the next token.
Over time, these parameters end up capturing grammar, facts, reasoning patterns, writing styles, and countless statistical relationships found in language.
More parameters generally allow a model to learn more complex patterns—but bigger doesn't automatically mean better.
The quality of the training data, the architecture, and the training process are just as important.
Does it actually think?
This is probably the most common question.
Not in the way humans do.
It doesn't have consciousness.
It doesn't have opinions.
It doesn't experience emotions.
It doesn't "understand" language the way people do.
Instead, it has become incredibly good at predicting what a useful, coherent response should look like based on everything it learned during training.
The results are so convincing that it often feels like you're talking to someone who is genuinely thinking.
But underneath, it's still performing one prediction after another.
Does it have memory?
Kind of.
During a conversation, the model can see your previous messages.
This is called the context window.
Everything inside that window helps it generate better responses.
Once those messages disappear from the context, the model doesn't automatically remember them.
Some AI applications add a long-term memory system on top of the model.
They store important information in a database, retrieve only the relevant pieces for future conversations, and include them in the prompt.
From your perspective, it feels like the AI remembered you.
In reality, it simply reread the information before answering.
Bringing it all together
When you strip away all the complexity, here's what an LLM really does:
- Break your text into tokens.
- Convert those tokens into mathematical representations.
- Look at the previous context using Attention.
- Predict the most likely next token.
- Repeat the process over and over again.
That's it.
And yet, when you combine billions of learned parameters, enormous amounts of training data, powerful hardware, and the Transformer architecture, this surprisingly simple process becomes capable of writing code, solving problems, translating languages, answering questions, and holding natural conversations.
Maybe that's the most fascinating part of all.
At its heart, an LLM is just predicting the next token.
But when you scale that idea up far enough, it starts to feel remarkably intelligent.