What Is a Large Language Model? An Everyday Guide

If you've used ChatGPT, Claude or Gemini, you've already used a large language model, often shortened to LLM. The name is a mouthful, but the idea is easier than it sounds, and understanding it explains both why these tools are so handy and why they sometimes get things confidently wrong.

A large language model in plain English

Wikipedia describes an LLM as an AI model, typically a neural network, trained on a vast amount of text for natural language processing tasks. Once trained, it can generate, summarise, translate and analyse text.

The "large" refers to the amount of text it learned from and the size of the model itself. The "language" part means words are its main material. Chat assistants are simply LLMs with a friendly chat window on the front.

How an LLM turns your question into an answer

The process has three simple stages.

1. Text becomes tokens. Wikipedia explains that text is mapped to a fixed vocabulary, with each entry given a number. IBM says these tokens can be whole words, parts of words or single characters.
2. The model predicts what comes next. Wikipedia notes that before fine-tuning, most LLMs work as next-token predictors, and GPT models are first taught to predict the next word.
3. It repeats, one piece at a time. Each predicted piece is added to the text, then the model predicts the next, until the answer is complete.

That's why the reply appears word by word on your screen. It's also the key to understanding mistakes: the model is producing text that fits the pattern, which isn't the same as looking up a verified fact.

How large language models are trained

IBM describes the first stage as self-supervised learning on unlabelled text, often billions or trillions of words. The model learns patterns of language by predicting missing or next words across all that material.

A second stage turns a raw text predictor into a helpful assistant. Wikipedia says models are refined to follow instructions, and IBM mentions methods such as reinforcement learning from human feedback, where people rate answers and the model is nudged towards the better ones.

If you want the data side in more detail, my explainer on where ChatGPT gets its information covers what the model has learned from and what it hasn't.

The LLM behind each assistant you might use

Wikipedia lists the models behind chatbots such as ChatGPT, Claude, Gemini, Grok, Qwen and DeepSeek, along with open-weight models such as Mistral 7B. The assistant is the product you chat with; the LLM is the engine under the bonnet. One company may offer several models in the same app, so check the vendor's own help pages for what's included.

What a large language model is good at for a small business

IBM lists text generation, summarisation, AI assistants, code generation, sentiment analysis and translation among the common applications. Day to day, that can mean:

• Drafting first versions of emails, web copy and social posts.
• Summarising long documents, meeting notes or customer feedback.
• Translating a message into another language.
• Tidying a rough idea into a clear plan or checklist.

Good instructions make a big difference to the result. My collection of ChatGPT prompt examples shows what a clear request looks like.

Where large language models go wrong

Wikipedia notes that generative LLMs have been observed to confidently assert claims of fact that their training data doesn't appear to support, a behaviour called hallucination. It also says biased or inaccurate training data can make output less reliable. IBM adds that LLMs need large amounts of computing power and energy.

• Made-up facts. Always check names, numbers, quotes and sources before you publish.
• Gaps in knowledge. A model only knows what it was trained on, plus anything you give it in the chat.
• Bias. The text it learned from carried human views, and so can its answers.
• Privacy. Read the tool's data controls page before pasting in customer details.

Questions to ask before relying on a large language model

Because every assistant sits on top of an LLM, the same short checklist works whichever one you choose. According to the vendors' own help pages, these are the things worth reading before you start:

• Which accounts and countries can use the tool, and which models come with it.
• What happens to the text you type in, and whether you can switch off history or training use.
• Whether it can browse the web or read files you attach, or only draws on what it learned in training.
• What the vendor itself says the tool can't do reliably.

None of that takes long, and it saves awkward surprises later. A sensible habit is to start with non-sensitive material, such as a public blog draft, before you trust a tool with anything about your customers.

Large language model, ChatGPT and AI: what's what

AI is the whole field. A neural network is one way of building it. An LLM is a neural network trained on text. ChatGPT is an app built on LLMs. Mixing the labels up is very common, so don't worry if you've done it.

The practical point for you: you don't choose an LLM the way you'd choose a spreadsheet. You try an assistant on your own tasks, see how well it copes, and keep a person responsible for the result. If you'd like help setting that up for your team, that's the work I do as an AI consultant.

Frequently asked questions

What is a large language model in simple terms?

It's an AI system trained on a vast amount of text so it can generate, summarise, translate and analyse language. Chat assistants like ChatGPT, Claude and Gemini are built on them.

How does an LLM work?

It breaks text into tokens and predicts what should come next, one piece at a time. Training on huge amounts of text teaches it which patterns of language are likely.

What is the difference between an LLM and ChatGPT?

The LLM is the underlying model. ChatGPT is a chat app built on top of such models.

Why do large language models make things up?

They produce text that fits patterns rather than checking facts, so they can state unsupported claims confidently. This is called hallucination.

Do LLMs understand what they write?

That's debated. What's clear is they predict language from patterns in training text, so it's wise to check anything important.