You have probably heard the terms ChatGPT, Gemini, Claude, Copilot, or generative AI thrown around constantly over the past couple of years. Everyone is talking about them, but very few people actually explain what is going on under the hood. This article breaks down what LLMs are and how they work — no math formulas, no academic jargon.
What does LLM mean
LLM stands for Large Language Model. An LLM is, at its core, a computer program trained to understand and generate text in natural language. That means it can write, answer questions, translate, summarize, explain, and hold a conversation — just like you would.
The key word in that definition is “trained.” An LLM is not programmed with pre-written answers to specific questions. Instead, it has “read” enormous amounts of text — books, articles, websites, source code, conversations — and learned patterns from all of it. That learning process is what gives it the ability to generate coherent and relevant responses.
How did it learn to write
Imagine learning to cook by reading millions of recipes. After a while, even without memorizing each recipe individually, you start to understand which ingredients work well together, which techniques are common, and what a good recipe looks like. LLMs work in a similar way.
During training, the model receives a piece of text and is asked to predict the next word. It makes a guess, sees the correct answer, adjusts its internal parameters, and tries again. This process is repeated billions of times, across billions of examples. By the end, the model has learned which words naturally follow others, in what contexts certain terms appear, and how to construct grammatically correct sentences and logical arguments.
There is no magic involved — just statistics at a very large scale. But the results are convincing enough to look like genuine intelligence.
What are parameters
When you hear “a model with 70 billion parameters,” the reference is to the internal numbers of the model — the numerical values that encode everything it learned during training. Think of them as a massive network of connections, similar to neurons in the brain, each with a certain “weight” that influences what response the model generates.
More parameters generally means more capacity to store knowledge and make complex connections. Large models like GPT-4, Claude, and Gemini Ultra have hundreds of billions of parameters. Smaller models that can run locally on a laptop have a few billion — enough for many tasks, but more limited when it comes to nuance and complexity.
How it works when you ask it something
When you type a question, your text is converted into numbers through a process called tokenization. Those numbers pass through the layers of the model’s neural network — each layer applies mathematical transformations and passes the result to the next one. At the end, the model calculates which word (or word fragment) has the highest probability of coming next.
It then adds that word to the response, recalculates, adds the next word, and so on — until the answer is complete. This is why LLMs generate text word by word (or token by token), rather than writing it all at once.
This also explains why they sometimes “hallucinate” — confidently stating facts that are simply not true. The model does not know what is real or false. It only knows what is statistically likely to follow in context. If an incorrect fact seems plausible based on patterns, it will write it with the same confidence as a real one.
ChatGPT, Claude, Gemini — what is the difference
All of them are LLMs, but they were trained differently, on different data, with different alignment strategies. After the base training on raw text, models go through an additional phase called RLHF (Reinforcement Learning from Human Feedback) — real people evaluate the model’s responses and guide it toward being more helpful, safer, and more accurate.
ChatGPT is made by OpenAI and popularized this technology starting in 2022. Claude is built by Anthropic with a focus on safety and longer, more nuanced responses. Gemini comes from Google and is integrated across their products. Copilot from Microsoft is largely GPT, embedded into Office and Windows. Each has different strengths, but the underlying principle is the same.
What they can and cannot do
LLMs are very good at writing, summarizing, translating, explaining concepts, brainstorming, generating code, and analyzing text. They are weaker at complex mathematical calculations, accessing real-time information (unless connected to the internet), and maintaining memory across separate conversations.
They do not “think” in any human sense. They have no consciousness, no intentions, and no real understanding of what they are saying. They generate plausible text based on statistical patterns. That is enough for a wide range of practical applications, but it is important to understand the limits.
Why this matters for you
Whether you are a designer, developer, business owner, or someone who just uses a computer — LLMs are going to be part of your toolkit in the coming years, if they are not already. Understanding how they work helps you use them more effectively: asking better questions, verifying the information you receive, and knowing when to rely on them and when not to.
You do not need to become a machine learning expert. But knowing that behind ChatGPT there is no human, no search engine, and no pre-written answer database — just a statistical model trained on text — gives you a real advantage in using it correctly.
The technology is already everywhere. Now you know what is under the hood.