If you have ever wondered what actually sits inside ChatGPT, the short answer is a large language model, usually shortened to LLM. But what is a large language model, in plain words? It is a computer program trained on a huge amount of text so that it can predict language — what word, sentence or paragraph is likely to come next. Everything else, from answering questions to writing poems, is built on top of that one skill.
Here is an analogy that helps. Imagine someone who has read a large library but never lived a life: they have seen how doctors talk, how lawyers write, how friends joke, how recipes are structured. Ask them to continue a sentence and they will do it convincingly, because they have seen millions of similar sentences. That is roughly what an LLM does. It has not experienced anything, but it has absorbed patterns from text at a scale no human could read.
What is a large language model? Breaking down the three words
Large means the scale. Modern LLMs are trained on trillions of words from books, websites, articles and code, and they contain billions of adjustable settings, called parameters, that store what the training taught them. Language means the subject: human language, in all its messiness — slang, grammar, multiple languages, even code. Model means it is a mathematical model, not a database: it does not look up answers, it generates them from learned patterns.
That last point surprises most people. An LLM does not contain a copy of Wikipedia it searches through. When it tells you the capital of France, it is producing the word its training says is most likely to follow your question in that context. Usually that is correct. Sometimes, when the pattern is less clear, it produces something plausible but wrong. This is the root of both the magic and the mistakes.
How training actually works, simply
Training happens in two broad phases. First comes pre-training, where the model reads enormous amounts of text and learns to predict the next word, again and again. This is not memorisation of whole books — it is learning the statistical shape of language: that “the capital of” is often followed by a place name, that a formal letter starts differently from a text message. Then comes fine-tuning, where the model is taught to be helpful: to follow instructions, answer questions, refuse harmful requests, and adopt a conversational tone. Human feedback plays a big role here — people rate responses, and the model adjusts toward the ones humans preferred.
The result is a general-purpose language engine. The same model can summarise a report, draft an email, explain a science concept, or write code, because all of those are, at bottom, language tasks. That generality is what made LLMs famous. Earlier AI systems were specialists; an LLM is a generalist that can be pointed at almost any text job.
Tokens: the model’s real unit
You may hear the word “token” and wonder what it means. An LLM does not read whole words the way you do. It breaks text into tokens — pieces of words, sometimes a whole short word, sometimes just a few letters. “Unbelievable” might be two or three tokens. This matters practically because most tools charge or limit you by tokens, and because a model’s memory — its context window — is measured in tokens too. A context window of 128,000 tokens is roughly a small book’s worth of conversation. When you paste a long document into a chatbot, you are filling that window.
If your chats start going strange after a very long conversation, that is often the context window filling up: the model starts losing track of the earliest messages. Starting a fresh chat, or summarising the key points and pasting them into a new one, usually fixes it.
Why LLMs sometimes sound so confident — and so wrong
An LLM’s job is to produce fluent, likely text, not to check facts. When you ask about something obscure, it will still produce a fluent answer, because silence is not an option it was trained to choose. This is why citations from a raw LLM should always be verified, and why tools that search the web before answering, like Perplexity, tend to be more reliable for current facts. Our guide to Perplexity AI explains that difference in detail.
It helps to compare models, too. ChatGPT vs Gemini vs Claude shows how different LLMs behave on the same kinds of tasks, and our DeepSeek explained post covers an open model that made headlines by matching much bigger rivals. If you want the bigger picture first, read what is generative AI.
Open vs closed models
Some LLMs are open: anyone can download and run them, study them, and build on them. Others are closed: you can only use them through a company’s app or API. Open models give you privacy and control — you can run one on your own computer — but they are usually smaller and need technical skill. Closed models are easier and often more capable, but your prompts go to someone else’s servers. Neither is automatically better; it depends on whether you value convenience or control more. For a beginner, a closed tool with a clear privacy policy is the simpler start.
Choosing and using a large language model well
Start with one mainstream tool and learn its habits before collecting five subscriptions. Write prompts with context: who you are, who the reader is, what format you want, and how long it should be. Break big jobs into steps — outline first, then sections, then an edit pass — instead of asking for a perfect final draft in one shot. And build a verification habit for anything factual: one trusted source, checked by you, beats ten confident paragraphs.
For deeper reading, Anthropic’s explainers and OpenAI’s research pages describe their models in accessible language. They are marketing documents as well as technical ones, so read them with that in mind, but they are still useful for understanding what each model is designed to do.
Frequently asked questions
Is an LLM intelligent? It is fluent and knowledgeable in a narrow sense, but it does not understand, intend or experience anything. Treat it as a powerful language tool, not a mind.
How big is a large language model? The biggest have hundreds of billions of parameters and were trained on trillions of words. Smaller models with a few billion parameters can still be very useful and can run on a laptop.
Do LLMs keep learning from my chats? It depends on the product and its settings. Some providers exclude your chats from training by default or offer an opt-out. Check the privacy settings before pasting anything sensitive.
A large language model is, at heart, a prediction machine trained on more text than any human will ever read. Understand that one idea and you understand why it writes so well, why it sometimes invents facts, and why the human in the loop — you — still matters more than ever.
To see what happens when a model like this is given tools and a goal, read our guide to AI agents explained.
