Large Language Model (LLM)
A large language model (LLM) is a language model trained on enormous amounts of text. It generates text by predicting, piece by piece, the most likely next chunk of text.
Also known as: Language model
How it works
An LLM is trained on very large amounts of text and learns statistical patterns of language along the way. When you send a request, it calculates piece by piece which chunk of text (token) is most likely to come next — that is how whole answers emerge. It only “knows” things to the extent that they are reflected in these patterns. Well-known model families include GPT, Claude, Gemini, Llama and Mistral.
A practical example
You give an LLM a customer’s email and ask it to draft a polite reply. The model writes a text that fits the request and the tone — without you having to write rules for every individual case.
What you should know
- An LLM is not a reference book: it can be convincingly wrong (see hallucination).
- Its knowledge ends at its training cutoff. Current or internal information has to be provided to it, for example via RAG.
- Models differ greatly in quality, speed, cost and data protection. Which one fits depends on the task.
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