Knowledge Cutoff: Definition and Examples

The knowledge cutoff is a fundamental concept for understanding the limits of large language models (LLMs). Each AI model is trained on a dataset collected up to a specific date. This date constitutes its "temporal boundary": everything that occurred after it is completely unknown.

For example, if a model has a knowledge cutoff in April 2024, it will not know the results of an election that took place in November 2024, nor the latest scientific advances published after that date. It might even confidently provide outdated information, as it has no way of knowing its data is stale.

This limitation has major practical implications in prompt engineering. When you ask a model about current topics, recent data, or events after its cutoff date, the answers can be incorrect or fabricated (hallucinations). That is why it is essential to know the knowledge cutoff of the model you are using.

To circumvent this limitation, several approaches exist: Retrieval-Augmented Generation (RAG) which injects up-to-date data into the context, real-time web browsing integrated into some assistants, or simply manually providing the necessary recent information in your prompt.