An LLM (Large Language Model) is an AI model that learns language patterns from large amounts of data. You can use it to generate, summarise, translate or analyse text. It can also write code. A convincing answer is not necessarily a correct one.
An LLM is the model behind many AI applications. A chatbot adds a conversation interface; an AI agent can combine the model with tools and actions. This distinction matters when you want to know what an application can actually do.
How does an LLM work?
A language model processes text as tokens: pieces of text that may contain part of a word, a whole word or punctuation. Predicting the next token is an important training task for many modern LLMs. During generation, the model uses the available context to produce a continuation.
The process broadly works like this:
- Training: the model receives many examples and adjusts its internal settings, the parameters, to learn patterns. The data used varies by model.
- Additional alignment: many models receive further training to follow instructions and align responses with desired behaviour. This can involve examples and feedback.
- Use: you send an instruction with context. The model generates an answer using the learned parameters. This use phase is called inference.
Many LLMs use a transformer: a model architecture that processes relationships between tokens through attention mechanisms. Not every language model has to be a transformer; LLM is a category, not a mandatory technical design.
A model does not retrieve its answers from a verified fact database by default. Its parameters contain learned patterns and knowledge, but also gaps and biases. The application can additionally consult a search engine, documents or a database.
LLM, ChatGPT, chatbot and AI agent: what is the difference?
An LLM is a model; a chatbot or AI agent is an application that can use a model. ChatGPT is a service, for example, not the general name for a language model. The capabilities you get depend on both the model and the application.
| Term | What it is | What it does not automatically do |
|---|---|---|
| LLM | Model that processes and generates text | Retrieve current information or take independent actions |
| Chatbot | Conversational application, possibly with search and tools | Know every source or verify every answer |
| AI agent | System that can use a model to operate tools and take actions within boundaries | Work safely without instructions, access permissions and checks |
| Agent Harness | Environment around an agent, including tool orchestration, state and checks | Replace the model's intelligence |
A model can propose a tool call. The surrounding software executes it and determines which permissions apply. For more on this relationship, read how AI agents work, what ReAct means for AI agents and what an Agent Harness does.
Can an LLM process images and audio too?
Some models can process or generate images, audio or video alongside text. Multimodal model is then a more precise term. Not every LLM supports all these formats, and input and output capabilities do not have to match.
An application that summarises a recording might use one multimodal model. It might also transcribe speech first and then send that text to a language model. The conversation interface does not always reveal how this works behind the scenes.
What can you use an LLM for?
An LLM is useful for tasks where language, structure and context matter. Examples include a first draft, a report summary or organising customer questions. You get assistance, not an automatic quality guarantee.
- Content: draft an outline or rewrite existing text using your brief and brand guidelines.
- Analysis: identify recurring topics in feedback or enquiries. Use reliable calculation software for exact arithmetic.
- Internal knowledge: find answers in approved documents, with references to the passages used.
- Service: prepare a draft answer that is reviewed before it reaches a customer.
- Code: propose, explain or help test code. Functionality and security still need checking.
A concrete example: ask an application to categorise anonymised customer questions by topic. Supply fixed categories, definitions and an 'unclear' category. Compare its classifications with a manually reviewed sample. This shows where the application helps and where it makes mistakes, without assuming accuracy.
Why can an LLM give incorrect answers?
An LLM can produce a plausible answer that is factually wrong or unsupported. This is called a hallucination. References, quotations and numbers can also be fabricated. Confident wording tells you little about reliability.
The model may lack knowledge, misinterpret a question or complete a pattern incorrectly. Search access does not entirely solve this: the application may find the wrong source or misuse a passage.
Check important claims against the original source. Ask what an answer is based on and whether information is missing. Have the application refer the question onwards or decline to answer when evidence is insufficient. Test that behaviour too, using examples where the correct answer is unavailable.
How do you use your own data: context, RAG or fine-tuning?
Your internal knowledge is not automatically available in an LLM. You can include information in an instruction, retrieve relevant documents or further train a model. These approaches do different things.
| Approach | What happens? | When might it fit? |
|---|---|---|
| Supply context | Add instructions, text or files to a request | A bounded task with a limited amount of information |
| RAG | Retrieval-Augmented Generation retrieves relevant passages and supplies them as context | Grounding answers in current or internal documents |
| Fine-tuning | Additional training changes the model parameters | Adjusting behaviour, style, format or task performance |
RAG does not change model parameters. Quality depends partly on the documents, retrieving the right passages and grounding the answer in them. Fine-tuning is not a substitute for an up-to-date knowledge source. You can combine these techniques; neither prevents all errors.
Does an LLM remember your conversations?
The context window is the amount of information a model can process during a request. An application can include previous messages in it. This is different from persistent memory or retraining the model.
Conversation history and memory are features of the service or application. They may store information and supply it again later. A conversation does not automatically mean the model parameters are immediately updated. A large context window also does not guarantee that every detail will be used correctly.
What should you consider when choosing an LLM application?
Choose based on your task, data and quality requirements, not just a model name. An application needs to handle your examples, constraints and review process.
- Task: what result do you need, and what counts as an error?
- Test material: use representative examples, including ambiguous questions and missing information.
- Review: assess facts, source use, required format and when human judgement is needed.
- Data: check access, retention and whether input is used for training. This varies by service, subscription and settings. Do not share confidential data without suitable agreements.
- Cost and speed: assess the whole application, including tokens, retrieval, tools and checks. The largest model is not automatically the best choice for every task.
- Actions: grant only necessary access and require approval before consequential actions.
Want to establish where language models make sense in your organisation? In a strategy session, we can look at your question through digital, marketing and AI. For more background, read the explanation of artificial intelligence.
Frequently asked questions
What does LLM stand for?
LLM stands for Large Language Model. It learns language patterns from large amounts of data and uses them to process and generate text.
Is ChatGPT the same as an LLM?
No. ChatGPT is an application that uses AI models and adds an interface and additional features. An LLM is the underlying model, not the complete service.
Does an LLM always have internet access?
No. Internet access is a capability an application adds through search or other tools. A model answer is not automatically based on current web information.
Does an LLM automatically learn from every conversation?
No. Using context, storing conversations and retraining a model are different processes. Whether conversations are later used for training depends on the service, settings and agreements.
Does RAG prevent hallucinations?
No. RAG can supply relevant source information, but the application can still retrieve the wrong passages or formulate an incorrect answer. Source checking and testing remain necessary.
Sources
This explanation draws on technical documentation and explanations from model developers and platform providers. Accessed on 21 October 2026.
Preferred source on GoogleRelated pages and articles
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