Glossary
Knowledge Cutoff
A knowledge cutoff is the date after which a language model has no training data; events, launches, and pricing changes past that date are unknown to the base model. Cutoffs explain many outdated brand claims — and why engines with live retrieval can be accurate while the same underlying model without browsing stays stale.
Every base model is a snapshot. If your product renamed its plans, changed pricing, or shipped a flagship feature after the cutoff, the model's parametric memory still holds the old world — and will present it confidently unless retrieval supplies the update.
This is why the same question can get three vintages of answer: a non-browsing chat answers from the cutoff era, a retrieval-backed engine answers from today's pages, and a hybrid blends both — sometimes citing your current pricing page while quoting last year's price from memory.
For brand monitoring, cutoffs set expectations and priorities: stale claims on non-retrieval surfaces are structural until the next model refresh, so the near-term fixes concentrate on what retrieval reads — current pages, current citations — while entity and content work pays off in the next training snapshot.
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