Knowledge Cutoff

Knowledge cutoffs matter less when an assistant searches the web for current or local information. They still shape the model's initial sense of which names are established. A newer business is more likely to appear through current web sources than through information absorbed during training.

A cutoff can also affect what the model searches for. The assistant may form an initial impression from training data and then retrieve evidence related to it. A model familiar with established businesses may be more likely to search for those names. Grounding can reduce this advantage but may not eliminate it.

Different facts become outdated at different rates. A long-established firm's specialty or a city's major hospitals may remain accurate for years. Staffing, pricing, hours, and whether a business still operates can change quickly. Assistants often recognize current or local questions and search for updated information.

A related trap for anyone measuring this channel: models frequently misreport their own cutoff, and the date a provider publishes describes the bulk of training data rather than a hard boundary. Treat any specific date as approximate, and never design a measurement that depends on knowing it precisely.

When accuracy matters, verify the answer against a current primary source even if the assistant claims to have recent knowledge. A published cutoff does not confirm that every fact before that date was included.

If a business changed important details after a model's training period, publish the current information on its website, profiles, and third-party sources that retrieval is likely to find.