LLM: What Is a Large Language Model and How Does It Work?
Large language models power ChatGPT, Claude and Gemini by predicting the next word. Here's how they work, why they hallucinate, and what the numbers mean.
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LLM stands for large language model. It’s a program that learned one skill from huge amounts of text: predicting what word should come next. This single trick powers ChatGPT, Claude, Gemini and every other AI chat. Below: how it really works, where hallucinations come from, what the numbers in model names mean, and why an LLM is not a knowledge base.
How It Works in One Paragraph
The model receives text converted to numbers, and its only job is to output a probability distribution for the next fragment. Then it appends that fragment to the input and calculates again, word by word, until it decides the response is complete. There’s no fact database or search engine inside; just a huge number of weights tuned to make predictions accurate. Every strength and weakness of AI chat flows from this one mechanism.
Where Hallucinations Come From
Since the model always outputs the most probable continuation, when you ask about something it doesn’t know, it produces a continuation that looks right: a nonexistent court ruling with a plausible case number, a book nobody wrote, a regulation that isn’t in the law. This isn’t a malfunction - it’s a direct consequence of how it works. That’s why anything with consequences must be verified at the source, and for factual questions you’re better off using tools that search first then answer, like RAG.
What the Numbers in Model Names Mean
| Term | What it means | Why it matters |
|---|---|---|
| Parameter count (e.g. 8B, 70B) | how many weights the network has, in billions | larger models usually reason better, but need more memory and power |
| Context window | how much text the model sees at once, measured in tokens | determines whether you can upload an entire contract or just a fragment |
| Knowledge cutoff date | how far the training data extends | without search, the model knows nothing about later events |
| Quantization | compression of weights to lower precision | lets you run a model on a laptop, at the cost of slight quality loss |
LLM vs Chat, Model vs Product
ChatGPT isn’t a model - it’s a product built around a model: it has conversation memory, search, tools, limits and an interface. The same underlying model can behave differently in two products because they differ in system instructions and available tools. That’s why comparing “ChatGPT versus Claude” compares products, not just models; we track current positions in our ranking.
Where LLMs Actually Help
In transforming text you already have: summarizing, rewriting, translating, extracting data from documents, writing first drafts. In tasks where speed of the sketch matters and verification is cheap. They struggle where you need exact memory and calculation: for arithmetic, dates and deadlines use tools, and use the model to operate them.
Technical data: Model Atlas: technical specs and pricing for each model.
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Does an LLM Think?
Not in the way humans think. It predicts the next text fragments, and the impression of reasoning comes from the fact that reasoning leaves regular traces in text that the model learned. This is a practical assessment, not a philosophical resolution.
Can You Run an LLM Locally?
Yes, smaller models run on ordinary computers; we cover this in our guides to LM Studio and Ollama.
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