What is a knowledge cutoff?
A knowledge cutoff is the date after which a model's training stopped, so it has no built-in awareness of anything published after that point unless a separate tool fetches it live.
Every model version has a knowledge cutoff: the point at which the text used to build it stops. Anything published after that date simply isn't part of what the model absorbed, from the model's own memory alone, it has no way to know it exists.
This explains a specific, common failure: an AI can state an old price, describe a discontinued product, or name someone who's left a company, confidently and without any warning, because its built-in knowledge is frozen at that cutoff. When an AI answer does reflect something current, this week's news, today's price, that's a sign a separate tool fetched it live at the moment of the question (see web-search-tool), not that the model's own knowledge had updated. It also follows that a model's cutoff date has nothing to do with whether a page written after that date can still be cited today: live citation runs through search at question time, not through training (see training-vs-search-crawler).
Related
- Training dataTraining data is the text a language model was shown while it was being built, and once training finishes it is compressed into the model's internal patterns rather than kept as documents it can look back up.
- Web search toolThe web search tool is the specific function that lets an AI assistant fetch and read live web pages mid-conversation, and it's what actually produces a citation rather than a memorized guess.
- Retrieval-augmented generation (RAG)Retrieval-augmented generation is the technique of retrieving relevant documents and giving them to a model as context before it answers.
- Training vs search crawlersA training crawler collects data models learn from. A search crawler builds the index an assistant cites when answering. Blocking one has nothing to do with the other.
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