Over the past few years, one type of AI has gone from research labs to everyday life almost overnight: large language models, or LLMs. If you’ve ever used ChatGPT, Gemini, or Copilot, you’ve used an LLM. These tools can write emails, answer questions, draft reports, or even tell stories in seconds.
To a beginner, they can seem like they’re thinking – but what’s really happening is more surprising and less mysterious. Understanding LLMs doesn’t require any coding or maths. You just need to know what they are trained on, how they use language, and where their strengths and limits lie.
What Is a Large Language Model?
A large language model is a type of artificial intelligence trained to predict words. That might sound simple, but when it’s done at a massive scale, it becomes incredibly powerful. LLMs are trained on billions of words from books, websites, articles, conversations and more. By analysing these examples, they learn patterns about how words and ideas usually fit together. When you type a question, an LLM doesn’t search the internet or recall exact facts. Instead, it predicts the most likely words that should come next based on everything it has seen before. This lets it generate fluent sentences on almost any topic – even ones it hasn’t seen directly.
Think of it like this: if you’ve read thousands of recipes, you could probably guess how to write a new one even without copying any. You’d know the structure (“Ingredients”, “Method”), common words (“mix”, “bake”), and how to phrase steps. LLMs do the same thing with all kinds of text, just at a far greater scale and speed.
How LLMs Are Trained
LLMs are built using a process called “training.” First, developers collect huge amounts of text data – anything from Wikipedia articles to published books to anonymised conversations. Each example is broken down into tiny chunks called tokens (pieces of words). The model’s job is to guess the next token over and over, adjusting itself every time it gets one wrong. It does this billions of times until it becomes very good at predicting text.
This process doesn’t give the model understanding or opinions. It’s not reading like a human does. It’s more like solving an enormous puzzle of word patterns, learning which pieces tend to follow which. The “large” in large language model refers to how many layers, connections, and training examples it has – the bigger the model, the more patterns it can store and recognise.
What Makes LLMs Different From Other AI
Many types of AI focus on specific tasks, like recognising images, playing chess, or detecting credit card fraud. LLMs are different because they are general-purpose text generators. Instead of being built for one narrow job, they can adapt to almost any text-based task: answering questions, summarising documents, writing stories, or explaining ideas. This flexibility is what makes them feel so versatile.
An image-recognition AI can only classify pictures. But an LLM can write about those pictures, draft an email about them, or even create a fictional story involving them. It’s still just predicting text, but it can apply that skill in many contexts. This is why tools like ChatGPT or Gemini can jump between roles so quickly – one moment they’re acting like a tutor, the next like a travel planner.
Everyday Examples of LLMs in Action
You’re probably using LLMs more often than you realise. Customer support chatbots often run on LLMs, replying to common questions in natural language. Word processors like Microsoft Word use them through Copilot to help draft and edit text. Search engines are starting to include LLM-generated summaries at the top of results pages. Even email apps now suggest full-sentence replies based on your past writing style.
These examples show why LLMs are useful: they can produce clear text on demand, but they also highlight an important point – LLMs don’t know if what they write is correct. They only know what sounds likely. This is why fact-checking is always important when you use them.
Strengths and Limits Beginners Should Know
LLMs are powerful, but they aren’t perfect. Their biggest strength is fluency – they can write almost anything quickly. They’re also very flexible, able to adapt to different tones, formats, and audiences. They can save hours on tasks like drafting emails, summarising notes, or planning content. Their biggest weakness, however, is accuracy. Because they generate text based on patterns, they sometimes make up information. This is called “hallucination.” They also reflect the biases in their training data, which can affect their tone or word choices. They don’t truly understand meaning, so they can be confident and wrong at the same time.
The Big Takeaway
Large language models are not thinking machines. They are extremely advanced pattern matchers, trained on vast amounts of text to predict what words are likely to come next. That simple idea, scaled up, is what makes them so impressive. Knowing this helps you use them wisely – they are tools to help you work faster, not sources of truth. If you keep that in mind, LLMs can be one of the most powerful assistants you’ll ever use.
If you want to understand how AI learns patterns in the first place, read How AI Actually Learns: A Beginner’s Guide to Training Data and Patterns. For an external overview, see Google’s beginner-friendly guide to large language models.
