How Does An LLM Actually Work?

Humans talk about AI as if it is some kind of magical technological brain that lives within an app. We ask it for advice, we let it write our emails, and we even use it to do our homework (sorry professors). But if you really want to get the most out of these life changing tools, you have to understand how it works under the hood.

The reality is that an Large Language Model (LLM) is not a person. It is not a database. It is a giant, incredibly sophisticated calculator. If you understand how that calculator was built and how it processes your words, you can stop getting frustrated when it doesn’t answer the exact question you had in mind, and start crafting perfect prompts.

The process of building an LLM happens in two main stages. The first stage is called pre-training, and you can think of it as the “firehose” phase. During this time, the model is fed a massive amount of data from multiple different sources. Its only job is to guess the next word in a sentence over and over again, billions of times. This is how it learns the structure of human language, the rhythm of how we speak, and the basic facts about the world.

The second stage is called fine-tuning, or the “finishing school.” This is where human trainers step in. They give the model specific instructions and rank its answers to help it become more helpful and polite. Without this step, the model would just be a raw prediction engine that might give you weird or toxic responses. It takes a massive amount of computing power and millions of dollars to crunch all this data into a usable model, which is why only a few companies in the world can build the top-tier ones.

So, where does all that information come from? It mostly comes from massive datasets like “Common Crawl,” which is essentially a snapshot of the public internet. It reads everything from blog posts and news articles to Wikipedia entries and scientific papers.

One of the most important parts of the “diet” is actual code. Even if an LLM isn’t specifically designed for coding, reading millions of lines of Python or C++ helps the model learn how to reason. Code is strictly logical: if A happens, then B must follow. By learning the logic of code, the model gets much better at following instructions in plain English.

It is important to remember that the model does not know things the way you and I do. It is a high-powered mirror of human language. When you ask it a question, it isn’t “thinking” about the answer. It is using math to predict what a correct answer should look like based on everything it has ever read.

This is why I call it a prediction engine. It is a giant calculator designed to guess the next piece of a sentence. It doesn’t have a soul or a consciousness: it just has a very high statistical probability of being right.

To understand how the guessing works, you have to understand tokens. Computers don’t actually see words like “apple” or “business.” They see numbers. Before the model even starts thinking, it breaks your sentence down into fragments called tokens.

A token is usually about four characters long, or roughly three-quarters of a word. The computer assigns a specific number to every possible token. So, “apple” might be number 17234. When you hear about “context limits,” it just means the model has a maximum number of tokens it can “remember” at one time before it starts forgetting the beginning of the conversation.

Once your prompt is turned into numbers, the game of prediction begins. If you type “The cat sat on the,” the model looks at the probability of every token in its library. It sees that “mat” has a very high probability, while “spaceship” has a very low one.

This is also where temperature comes in. If the temperature is low, the model will always pick the most likely word, which makes it very factual and predictable. If the temperature is high, it might take a “creative” risk on a less likely word. This is also why LLMs hallucinate. Since the model only cares about what sounds statistically right, it can easily make up a fake fact that perfectly fits the rhythm of a professional-sounding sentence.

Now that you know how the engine works, you can see why the way you write a prompt matters so much. Giving the model context is like giving a GPS a destination. If you just say “write a post,” the model has too many high-probability paths to choose from.

If you tell the model it is a Senior Marketing Manager and give it a specific goal, you are narrowing down the statistical paths it can take. You are telling the calculator to only look at the part of its brain that sounds like a professional manager. By providing better input, you are literally forcing the math to give you a better output.

You don’t need a PhD in computer science to use these tools effectively. But you do need to respect the process. When you realize that the AI is just a world-class guessing machine fueled by a massive library of human data, it changes how you interact with it. You stop expecting it to be a magic oracle and start treating it like a very fast, very capable assistant that needs clear instructions to do its best work.