Behind every AI chatbot that drafts an email or answers a question sits a large language model, or LLM. You do not need to be technical to understand the basics, and knowing them helps you see why AI tools are sometimes impressively useful and sometimes confidently wrong.

A large language model (LLM) is an AI system trained on vast amounts of text so that it can understand and generate human language. It is the engine behind many chatbots and assistants, and it powers much of today’s generative AI for text.

An LLM in plain English

Imagine an extremely well-read assistant who has absorbed an enormous library and learned the patterns of how people write and explain things. That assistant can draft, summarise, translate and answer questions fluently. But unless it is given a way to look something up, it works from what it learned, not from a live check of the facts.

The word “large” refers to the huge amount of text used in training and the very large number of internal settings, called parameters, that the model adjusts as it learns.

How an LLM works, step by step

1. It is trained on huge collections of text. This can include books, articles, websites, code and other written material.
2. It learns patterns. The model picks up how words, ideas and facts tend to relate to each other.
3. You give it a prompt. This is the question or instruction you type.
4. It breaks the prompt into small pieces. These pieces are often called tokens.
5. It predicts what comes next, repeatedly. One piece at a time, it builds a response that fits the prompt and the patterns it learned.
6. It is refined after training. Developers and human reviewers shape the model to be more helpful, safer and better at following instructions.

The key point is in step five. An LLM generates likely language. It is not copying an answer from a verified database.

What LLMs are good at

• Summarising long documents and email threads
• Drafting and rewriting text in a chosen tone
• Translating between languages
• Explaining concepts in simpler terms
• Brainstorming ideas, headlines and outlines
• Sorting and extracting information from text
• Helping with code and spreadsheet formulas

Where LLMs fall short

• Hallucinations. They can produce statements that sound convincing but are wrong, including invented facts, names or sources.
• Knowledge cut-offs. An LLM learns up to a certain date. Without a search tool, it may not know about recent changes.
• No real experience. It has not worked with your customers or visited your premises, so it can only describe what it has read.
• Inconsistency. Ask the same question twice and you may get different answers.
• Bias. Models can reflect biases in the material they learned from.
• Limited memory in a conversation. An LLM can only consider a limited amount of text at once.
• No access to your private information unless you provide it.

Why LLMs sometimes make things up

Because an LLM is built to produce plausible language, it will often keep going even when the information it needs is thin or missing. Instead of saying “I don’t know,” it may fill the gap with something that sounds right.

This matters for your business. If the information available about you online is unclear, outdated or contradictory, an AI tool may fill in the gaps incorrectly. Clear, consistent public information gives it less reason to guess. That is the idea behind AI visibility and GEO.

LLMs and AI search

Many AI search tools combine an LLM with a search step. The system first finds relevant pages, then asks the LLM to write an answer based on what it found. This approach is often called retrieval-augmented generation, and it is why some tools can discuss recent events and show citations beside their answers.

Without that search step, a chatbot relies mainly on its training, which is why its answers about local businesses or recent changes can be less reliable.

The tool is not the model

It helps to separate the product you use from the model underneath. ChatGPT, Gemini, Claude, Copilot and Perplexity are products. Each is powered by one or more LLMs, and companies update models often. Names and features change quickly, so it is more useful to understand how they behave than to memorise a list.

What LLMs mean for your business

• Customers use them. People ask AI tools for recommendations and explanations before contacting a provider.
• Your team may use them. They can speed up drafting, summarising and brainstorming, with proper review.
• They describe your business. What they say depends on the information they can find about you.
• They need good input. The clearer your information and instructions, the better the output.

Practical steps:

1. Make your public information clear, consistent and current.
2. Use LLM tools internally for first drafts, with a person reviewing everything.
3. Protect private and client data, and check each tool’s privacy settings.
4. Regularly check what AI tools say about your business.

How to write better prompts

• Be specific. Say what you want, for whom and why.
• Give context. Share relevant background about your business, audience and goals.
• State the format and tone. For example: “Write three short bullet points in a friendly, practical tone.”
• Provide examples. Show a sample of the style you like.
• Ask it to check with you. Invite it to ask clarifying questions before starting.
• Review and refine. Treat the first answer as a draft and ask for improvements.

If you want the output to sound like your business, give the model your brand voice description and a few sample sentences.

Common mistakes

• Treating an LLM like a search engine that always returns verified facts
• Publishing its output without checking accuracy
• Assuming it knows your business, pricing or local details
• Pasting sensitive information into tools without checking privacy terms
• Assuming all AI tools behave the same way

Frequently asked questions

Is an LLM the same as AI? No. An LLM is one kind of AI, focused on language. Other AI systems work with images, audio, video or data.

Is ChatGPT an LLM? ChatGPT is a chatbot product that uses LLMs. The same is true of Gemini, Claude and Copilot.

Do LLMs learn from my conversations? It depends on the tool and its settings. Check each product’s privacy policy and account controls before sharing anything sensitive.

How does an LLM know about my business? It may know a little from its training data, and, when it has a search function, from pages it finds at the time of your question. That is why your website, listings and reviews matter.

Where to go next

Learn how this technology fits into the bigger picture with generative AI and AI search. To see what it means for your business, read about GEO, citations and AI visibility.

Wondering what AI tools say about you? Talk to us or return to the Marketing Terms Glossary.