How do large language models work?
A large language model (LLM) is trained on huge amounts of text to predict likely continuations of language. From that, it can answer questions, write, and summarize — but it predicts patterns, it doesn't look up facts.
The core mechanism
An LLM learns statistical patterns in language during training. When you prompt it, it generates a response by predicting plausible next pieces of text based on those patterns and your input.
Why this explains its quirks
- It can sound authoritative while being wrong (it predicts, not verifies).
- Clear context dramatically improves results.
- It has limits on how much it can 'hold in mind' at once.
Understanding that an LLM predicts language — rather than retrieving truth — is the key to using it well and catching its mistakes.
Picture an extraordinarily well-read autocomplete: brilliant at continuing your thought, but it's matching patterns, not consulting a fact database.
- Fluent answers can be fabricated (hallucinations).
- Outputs vary with phrasing and context.
- It is not a reliable source of current or precise factual data without verification.
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Last reviewed 2026-06-25. This topic can change over time; always confirm current specifics from primary sources.