The date after which a model has no built-in knowledge, because its training data stops there; recent facts are unknown unless supplied through grounding or retrieval.
Knowledge Cutoff is the date after which a language model has no built-in knowledge, because its training data stops there. Anything later is unknown to the model unless it is supplied at request time, so from memory alone a model cannot reliably answer questions about events, prices, or releases past that point.
The cutoff is a property of the trained weights, distinct from the current date. A model can know today's date and still hold a world frozen months or years earlier. This is the gap that grounding and retrieval-augmented generation exist to close: when a question turns on recent facts, the system runs a live search and answers from retrieved pages rather than from its stale prior. Asked about recent events without retrieval, a model is prone to hallucinate a plausible but wrong answer.
For AI visibility the cutoff is why fresh, current content earns a place in answers a model cannot produce from memory. A brand, product, or fact that postdates the cutoff exists for the model only through what it can retrieve, which puts a premium on being crawlable and citable at the moment the question is asked.
