What are large language models and how do they work? A plain guide for beginners

You type a question into a chat window and, a few seconds later, a well-organised answer appears in fluent language, as if someone who had read a great deal were sitting behind the screen. That is what tools such as ChatGPT, Gemini and Claude do, and the technology behind all of them is called a large language model. In this article we explain, in plain words, what that phrase means, how a model learns, why it sometimes seems brilliant and sometimes gets things wrong, and how to use it for study and work with a clear head rather than with amazement alone.

What is a language model in the first place?

Before we get to the word “large”, let us understand the idea of a “language model”. A language model is a program that has learned, from a very large amount of text, to predict the next word in a sentence. If you write “The student went to the”, the model weighs several possibilities: “school” is very likely, “library” is quite likely, and “moon” is very unlikely. This simple-sounding task – predicting what comes next – is the foundation on which every ability you see today has been built.

You might ask how predicting the next word can produce a whole essay or solve a programming problem. The answer is that the model repeats the process again and again. It predicts one word, adds it to the text, predicts the one after that, and so on. And to predict well across an enormous variety of text, it is forced during training to pick up grammar, the relationships between ideas, and the patterns of reasoning that are common in what it has read.

So why do we call it “large”?

The word refers to two things together: the amount of data the model was trained on, and the number of parameters inside it. Parameters are numbers that training adjusts little by little; you can picture them as tiny dials that control how the model processes text. Modern models contain billions of these parameters and have been trained on text from books, websites, articles and source code in quantities no person could read in many lifetimes.

How does a model learn? Three main stages

Building a modern language model usually goes through several stages. The details differ from one company to another, but the general picture below is enough to understand what happens:

Stage one: pre-training

At this stage the model reads huge amounts of text and learns to predict the next word. Nobody writes grammar rules for it or explains what words mean; it infers the patterns by itself from repetition. At the end of this stage we have a model that continues text fluently, but it does not yet know how to behave like an assistant that answers questions politely and clearly.

Stage two: instruction tuning

Here the developers show the model carefully written examples: a question, followed by a good answer to it. The aim is for the model to learn the shape of a helpful conversation – to follow instructions, stick to what was asked, and organise its answer.

Stage three: tuning with human feedback

The model produces more than one answer to the same question, and human reviewers pick the best one. Those preferences are then used to steer the model towards answers that are more useful and safer. This technique is known by the abbreviation RLHF – reinforcement learning from human feedback.

Tokens and the context window: two ideas you will hear about often

A model does not read words the way we do. Text is first split into small pieces called tokens. A token can be a whole word, part of a word, or a punctuation mark. Common English words are often a single token, while many Arabic words are split into more than one token because of the prefixes, suffixes and patterns of the language. That is why, with some models, Arabic text uses more tokens than English text of the same length.

The context window is the largest number of tokens the model can “see” at one time: your message, the earlier conversation, any document you pasted, and then the answer it writes. If a conversation grows very long and exceeds the window, the model may “forget” what was said at the start. So when you work on a long task, it helps to summarise what you have agreed so far and present it again in a fresh message.

Transformers: the idea that changed everything

Most of today’s large language models are built on an architecture called the transformer, introduced in a 2017 research paper titled “Attention Is All You Need”. Its central idea is the attention mechanism: when the model processes a word, it looks at the other words in the text and decides which of them matter most for understanding it. In the sentence “Ahmed put the book on the table because it was heavy”, attention helps the model link “it” to the book rather than the table.

If you would like to see this idea visually, the following video from the 3Blue1Brown channel is one of the clearest explanations available:

Why does a model sometimes get things wrong with confidence?

One of the most important things to know is that the model does not “look up” the truth in a database; it generates likely text based on what it learned. Most of the time the likely text is correct, because correct information is repeated often in the training data. But when it is asked about rare details, exact numbers or specific references, it may produce an answer that sounds entirely convincing and is not true. This is called hallucination.

How to protect yourself from hallucinations

• Ask for the source, then check it yourself: if the model names a book or a study, look it up. Sometimes the references themselves are invented.
• Do not rely on it for sensitive figures: check statistics, dates and prices against their official source.
• Give it the text instead of asking from memory: if you paste the document and ask it to answer only from that, errors drop a great deal. That is the idea behind the technique explained in our article on retrieval-augmented generation (RAG).
• Ask it to say what it does not know: a line such as “if you are not sure, say so plainly” improves results in many cases.

Practical uses, depending on your role

If you are a student

Use the model as a patient tutor that explains an idea in more than one way, generates revision questions, or reviews your writing style. But be careful not to let it write your homework for you; the point of homework is for you to learn, and the model will not be next to you in the exam. A useful approach: write your own answer first, then ask it to point out the weak spots without rewriting it.

If you are a teacher

A model can help you prepare lesson plans, vary your examples and write questions at different levels of difficulty. Your review remains essential to make sure the content is scientifically accurate and suits your students’ ages and culture.

If you run a business

Small businesses use models to write first drafts of content, answer frequently asked questions and summarise meetings. A golden rule here: never put your customers’ personal data or your business secrets into a tool whose privacy policy you do not know.

How to start your journey in this field

Understanding language models is an excellent way into the world of artificial intelligence, but it is not the end. The natural next step is learning to write prompts well, which we explain in detail in our article on prompt engineering. After that you can meet AI agents, which carry out multi-step tasks. If you want an organised plan from the beginning, read a beginner’s map for learning AI, or try the Find your path tool to get a plan that suits your level and interests.

In the artificial intelligence course at Badr Academy Education we move from theory to practice: trying models, building small tools, and knowing when to use AI and when not to. You will also find hand-picked resources that complement what you learn in the learning library.