A knowledge cutoff is the latest point represented in a language model’s training data. After that point, the model’s learned knowledge is frozen unless external information is supplied. section 5.1.1
Treat a cutoff as a product constraint:
- Expect gaps around breaking news, recent trends, and updated facts. section 5.1.1
- Prefer a later cutoff when recency matters, then connect the model to real-time data sources. section 5.1.1
- Add output validation, fact checking, or human review when accuracy is important; retrieval-augmented generation can reduce factual errors. section 5.1.1
A time-sensitive communications product
A company generating public-facing content cannot rely solely on a model’s frozen knowledge. It should supplement the model with current data and verify claims before publication. section 5.1.1
