Knowledge cutoff
The date after which an AI model has seen no data, so it knows nothing newer.
A model's knowledge cutoff is the point in time where its training data ends. An LLM learns from a huge snapshot of text collected up to a certain month; whatever happened after that is simply not in the model. Ask it about last week's news or the newest version of an app and it either says it does not know or, worse, guesses.
The gap cannot be avoided. Collecting data, training and testing a model takes months, so on the day a model is released its cutoff is already in the past, and it stays frozen while people keep using the model for a year or more. Providers usually publish the cutoff in their documentation. It is also blurrier than it sounds: the last months before the cutoff are thinly covered, because the internet had not finished writing about them yet.
The usual fix is not retraining but handing the model fresh information when you ask: a web search tool, RAG over your own documents, or simply pasting the relevant text into the prompt. When a chatbot gets a recent fact right, that is usually how. When it confidently states something outdated, the cutoff is a likely reason, so it is worth checking anything that may have changed.
Keep in mind
- Training data stops at a date; the model knows nothing after it.
- The cutoff is always months before the model's release.
- Search tools, RAG or pasted text give the model fresh facts.
- Outdated answers stated with confidence are a classic symptom.
