Knowledge cutoff
Also called training cutoff, cutoff date, knowledge cut-off
The date after which an AI model has no built-in knowledge, because its training data stops there.
A model's knowledge cutoff is the latest date its training data covers. Events, product releases, prices, laws and research after that date are unknown to the model unless it is given them in the prompt or through a tool such as web search. Cutoffs are usually several months to a year before a model's public release, and models stay in use long after release, so the gap grows over time.
Models are not always reliable about their own cutoff. Information near the cutoff is sparse in the training data, because the web had not finished writing about it yet, so a model may know less about its last few months than the stated date suggests. Models can also be confused about the current date if it is not in the prompt.
Most chat apps now include the current date in their system prompt and offer web search, which helps a lot. But when a model answers without searching, its answer reflects the world as of its training. Anything time-sensitive, such as pricing, regulations, software versions, sports results or who holds a job, deserves a check.
Example
You ask about the latest version of a software library. The model confidently names a version from a year ago and describes features that have since been renamed. With web search turned on, it finds the current release notes instead.
Common mistake
Asking the model whether its information is current and trusting the answer. A model can not see what it does not know. Use search-enabled modes, give the date and relevant recent documents yourself, or verify time-sensitive facts with an up-to-date source.