How Do Large Language Models Work?

Chat tools such as ChatGPT and similar assistants are built on a type of model called a large language model (LLM). They can draft emails, summarise documents or explain concepts, but they also sometimes state wrong information in a very confident tone. To use them effectively and safely, it helps to understand in broad terms what they actually do inside.

The core idea: predicting the next piece of text

At its heart, a language model is trained to answer one very simple question: given the text so far, what is most likely to come next? When writing a long answer, the model predicts one small piece at a time, appends that piece to the text, and repeats the process. The coherence we see is the result of repeating this prediction step many, many times.

Tokens: the units the model actually reads

A model does not read letters or words the way humans do. Text is cut into pieces called tokens, which may be a whole word, part of a word or a punctuation mark. Each token is converted into a sequence of numbers, and the model works on those numbers. A practical consequence is that a model can only consider a limited number of tokens at once, usually called the context window. If a conversation or document is too long, the earliest part may be ignored.

The transformer architecture and attention

Most of today's large language models are based on the transformer architecture. Its key element is the attention mechanism, which lets the model weigh how relevant tokens in a text are to one another. This is how, on meeting a pronoun such as "it" in a sentence, the model can work out which earlier object "it" most likely refers to. During training, the model's billions of parameters are gradually adjusted so that it predicts the next token more accurately across a very large body of text.

From raw model to chat assistant

A model trained only to predict text usually does not hold a conversation well. To become a useful assistant, it typically goes through additional tuning steps in which it is taught from example dialogues and adjusted according to human judgements about which answers are more helpful, truthful and safe. The behaviour of an AI assistant therefore reflects both its training data and the choices made by its developers.

Why can a model "make things up"?

Because the model is optimised to produce linguistically plausible text rather than to look up a database of facts, it can generate answers that sound convincing but are wrong. This phenomenon is commonly called "hallucination". It tends to occur with specific details such as names, figures, quotations or references. Some systems reduce the problem by letting the model consult external documents before answering, but no approach removes the risk entirely.

A few useful habits:

• Verify figures, proper names and quotations against reliable sources.
• Provide context and relevant material in your question so the model stays closer to it.
• Treat the output as a draft to edit, not a final answer.

Conclusion

A large language model is a system that predicts the next token using statistical patterns learned from a vast amount of text. It is very strong at drafting, rephrasing and suggesting ideas, but it does not "understand" the world the way people do and it does not guarantee correctness. Knowing this lets us make the most of the tool while keeping the roles of checking and final decision-making for ourselves.