LLM
Also called Large language model
The kind of AI behind chatbots — trained on mountains of text to predict what word comes next, one token at a time.
Think of it like
Autocomplete that read the whole internet and never gets tired of finishing your sentence.
Example
ChatGPT, Claude, and Gemini are all LLMs wearing different coats.
How it actually works
“Large” refers to the parameter count — billions to trillions of learned weights. The whole thing is one trick applied at scale: given some text, guess the next token, over and over, until it has written a paragraph. Everything else — reasoning, coding, translation — is that trick generalizing further than anyone expected.
For product teams
The core engine you are building on — general-purpose, capable, and priced per token.
For engineers
A transformer trained with next-token prediction at scale; billions of parameters, autoregressive decoding at inference.
Related
- Foundation model — A specific, huge example of a foundation model.
- Tokens — Everything it reads and writes is counted in tokens.
- Hallucination — Its confident wrong answers are hallucinations.
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