An LLM (large language model) is a neural network trained on huge amounts of text that generates text by predicting, piece by piece, what is most likely to come next. This gives it abilities such as writing, summarising, translating and programming. Because the model works on probability rather than fact-checking, it sounds convincing even when it is wrong.
How does an LLM work?
An LLM splits text into tokens, small word pieces. During training it sees enormous amounts of text and learns to predict the next token as well as possible. This produces billions of parameters, numbers in which patterns of language, relationships and writing styles are stored. Most of today's models are based on the transformer architecture introduced in 2017 in the paper "Attention Is All You Need".
On a request, known as inference, the model calculates the most likely next token, appends it and repeats the process until the answer is complete. That is why text appears word by word. Providers often bill by tokens processed; more in Tokens and context windows explained.
| Phase | What happens | Who does it | Consequence for you |
|---|---|---|---|
| Training | The model learns from huge amounts of text | The model provider, with heavy computing | Knowledge has a cut-off date |
| Fine-tuning | The model learns to follow instructions | The provider | It answers helpfully, not necessarily correctly |
| Inference | The model answers your request | Provider servers or your own machine | Every use consumes computing power |
| Context | You supply documents and instructions | You | Only what you supply is certainly known |
Why does an LLM sound confident even when it is wrong?
An LLM is trained to produce plausible language, not to check truth. If it lacks information, it often fills the gap with something that sounds fitting. This is called hallucination. In addition, it rarely expresses uncertainty by itself. How to deal with this is described in AI hallucinations: what they are and how to protect your business.
Further limits: knowledge ends at the training cut-off, long documents are not always fully taken into account and the same question can produce different answers. On some plans, confidential inputs may be stored or used for training.
What is an LLM good for in a business?
LLMs suit tasks in which a person checks the result: drafts of emails and texts, summaries of long documents, translations, sorting and labelling enquiries or extracting details from text. Where company knowledge is involved, such as from manuals or price lists, the model is made to look things up in documents. This technique is called retrieval-augmented generation (RAG).
Less suitable are tasks in which an error goes unnoticed and becomes expensive, such as legal and tax information or promises to customers without a check.
- Task chosen in which a person checks the result
- Necessary context and desired format included in the request
- Figures, names and sources cross-checked
- Provider's data protection terms read
- No confidential data entered in plans that store or train on it
- Rule set for who approves results
How do you choose a model?
Models differ in quality, speed, cost, data protection terms and ways of connecting them. No model is best at everything. Test two or three candidates with your own tasks and compare result, speed and price. A guide is in How to evaluate a new AI model.
Conclusion: useful with checking
An LLM is a powerful language tool with clear limits. Use it for drafts and routine work, supply context and check the results. How to build a business process from it is shown by our AI automation service. If you want to know what makes sense in your case, describe your task.
