What Is a Large Language Model?

What Is a Language Model?

Imagine you’re typing a text message and your phone suggests the next word. It might offer “you”, “the”, or “tomorrow” based on what you’ve already written. That’s a language model in its simplest form — a system that predicts what comes next in a sequence of text.

A large language model (LLM) takes this idea and scales it dramatically. Instead of simple word frequency statistics, it uses a deep neural network trained on vast amounts of text to learn patterns in language — grammar, facts, reasoning styles, even coding conventions. When you ask an LLM a question, it generates a response one token at a time, each time predicting the most likely continuation given everything that came before.

Why “Large”?

The “large” in LLM refers to two things: the size of the model and the scale of training data.

Model size is measured in parameters — the numbers inside the neural network that get adjusted during training. Early language models had millions of parameters. Today’s large models have billions to trillions. GPT-4 is estimated to have over a trillion parameters. More parameters means the model can capture more nuanced patterns.

Training data is equally vast. LLMs train on large portions of the internet — books, articles, code repositories, forums, documentation. The combination of massive models and massive data produces what researchers call emergent abilities: capabilities that appear at scale but aren’t present in smaller models, like multi-step reasoning, code generation, or translation between languages the model wasn’t explicitly trained for.

How Do They Work?

At a high level, the process has two phases:

Pretraining is where the model learns language. It reads enormous amounts of text and learns to predict the next token (word or word-piece). This simple objective — guess what comes next — turns out to be remarkably powerful. To predict well, the model must learn grammar, facts, logic, style, and context. This phase takes weeks on thousands of specialized processors and costs millions of dollars.

Inference is when you use the model. You provide a prompt (your input), and the model generates tokens one at a time. At each step, it calculates probability scores for every possible next token and picks one. This is why LLM responses aren’t deterministic — there’s a degree of randomness (controlled by a setting called temperature) in which token gets selected.

Key Terminology

Token — The basic unit of text an LLM works with. Not always a full word — “unhappiness” might be split into [“un”, “happiness”]. See the Tokenization concept for details.
Parameters — The adjustable numbers inside the neural network. More parameters generally means more capability (and more computational cost).
Pretraining — The initial training phase where the model learns from raw text data.
Fine-tuning — Additional training on specific tasks or styles after pretraining. This is how base models become helpful chatbots.
Inference — Using a trained model to generate predictions or responses.
Context window — The maximum amount of text the model can consider at once, measured in tokens.

Why Does It Matter?

LLMs are the engine behind the current AI revolution. They power chatbots like ChatGPT, Claude, and Gemini. They generate code in tools like GitHub Copilot and Cursor. They summarize documents, translate languages, analyze data, and assist with creative writing.

Understanding what LLMs are and how they work — even at a conceptual level — helps you use them more effectively. When you know that an LLM is predicting the next token based on patterns in its training data, you understand why clear prompts get better results, why models sometimes confidently state incorrect facts (hallucination), and why they can’t truly “know” or “understand” in the way humans do.

Common Misconceptions

“LLMs understand language.” They process and generate language with impressive sophistication, but they don’t understand meaning the way humans do. They learn statistical patterns. Whether this constitutes a form of understanding is an active debate, but it’s important not to assume human-like comprehension.

“LLMs are databases of facts.” They don’t store and retrieve facts like a database. They learned patterns during training, and those patterns encode factual information — but imperfectly. This is why they can be confidently wrong (hallucinate).