In plain English
Training data has to be gathered and frozen at some point before a model is built, and that point is the knowledge cutoff. Anything that happened afterwards is simply absent: an election result, a product launch, a change in the law. The model is not aware of the gap, so rather than saying it does not know, it will often answer from whatever it learned before the cutoff and sound entirely certain.
Why it matters
It is the single most common reason a confident answer turns out to be out of date, and it catches people out on anything involving prices, regulations, or current events.
A worked example
A model with a cutoff in early 2024 is asked who holds a particular office and names the person who held it then, with no indication that it might have changed since.
Common confusion
A cutoff is not the same as being offline. Many assistants can search the web to fill the gap, but the underlying model still has a fixed cutoff, and what it recalls unaided stops there.