ChatGPT, Claude, Gemini, Llama and Mistral are all built on the same core technology: the large language model, usually shortened to LLM. You do not need a computer science degree to understand the basics, and knowing how these systems work makes you much better at using them.
Updated September 2026: we added links to the research on scaling, training for helpfulness and why models hallucinate.
The one-sentence version
A large language model is a computer program trained on huge amounts of text to predict what comes next in a sequence of words, and that simple skill, scaled up enormously, turns out to produce something that can write, summarize, translate, reason through problems and hold a conversation.
Step one: learning from text
During training, the model is shown vast collections of text: books, websites, articles, code and other sources. Over and over, it is asked to predict the next piece of text, and each time it is nudged to make better predictions. These nudges adjust billions of internal numbers called parameters.
No one programs in grammar rules or facts directly. Instead, to get good at prediction, the model has to pick up patterns: how sentences are structured, how arguments flow, which facts tend to appear together, how code is written. That is why the “large” matters. More data, more parameters and more computing power have generally produced more capable models. A 2020 OpenAI study found that a language model’s error falls in a predictable way, following a power law, as model size, data and computing power grow.
Step two: learning to be helpful
A model trained only to predict text is not a good assistant. Ask it a question and it might continue with more questions. So developers add further training stages, using examples of helpful conversations and feedback from people who rate responses. This is where a model learns to follow instructions, answer clearly and decline harmful requests. In OpenAI’s InstructGPT research, people preferred answers from a model trained this way over those from a model 100 times larger that had not been.
How it writes an answer
When you send a message, the model breaks your text into small chunks called tokens, which are often pieces of words; in English, one token is roughly three-quarters of a word. It then generates a response one token at a time, each time choosing a likely next token based on everything that came before. Some randomness is usually involved, which is why asking the same question twice can produce different wording.
A useful mental model Think of an LLM as an extraordinarily well-read writer with no notes in front of them. They can explain, draft and reason impressively, but when they are unsure of a detail, they may fill the gap with something that sounds right rather than admitting they do not know.
Why LLMs make mistakes
Because the model is producing plausible text rather than looking facts up in a database, it can state false things with complete confidence. This is often called hallucination. It is most common with specific details such as names, dates, statistics, quotes and citations. OpenAI researchers argued in 2025 that part of the cause is how models are graded: when evaluations reward only correct answers, models are encouraged to guess rather than admit they do not know.
Modern assistants reduce this problem by connecting the model to tools: web search, uploaded documents, calculators and code. When a model can read a source before answering, its accuracy on factual questions improves considerably. That is the idea behind retrieval-augmented generation, first described in 2020. It still pays to check anything important.
What “knowledge cutoff” means
A model learns from data collected up to a certain date. Events after that date are unknown to it unless it has access to live search. If an assistant gives you outdated information about prices, laws or current events, this is usually why.
What LLMs are good at
- Drafting and rewriting text in different styles and lengths
- Summarizing documents you provide
- Explaining concepts at the level you ask for
- Brainstorming options and outlining plans
- Writing and explaining code
- Translating and adapting tone between languages
Where to be careful
- Exact facts, figures and citations without a source
- Arithmetic done “in its head” rather than with a calculator tool
- Medical, legal and financial decisions, where expert review matters
- Anything where a confident wrong answer would be costly
The takeaway
Large language models are neither magic nor simple autocomplete. They are powerful pattern learners that have absorbed an enormous amount of human writing. Use them for what they are good at, give them the information they need, and verify the details that matter. Our guide to writing better prompts is a good next step.
Sources
- Scaling Laws for Neural Language Models, arXiv, January 2020
- Training language models to follow instructions with human feedback, arXiv, 2022
- What are tokens and how to count them?, OpenAI Help Center
- Why language models hallucinate, OpenAI, September 2025
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, arXiv, 2020
