Knowledge Cutoff (LLM)
The knowledge cutoff of a {{large language model}} is the date beyond which its training data was not collected. Information about events after the cutoff is absent from the model's weights and can only be supplied via tools or retrieval. Cutoffs are set during dataset assembly and reported by the model provider.
A knowledge cutoff is the calendar date used as the upper bound on the training corpus of a large language model. Web crawls, books, code repositories, and other sources are filtered to drop documents dated after this point, so the model has no memorized representation of events that happened later. The cutoff is announced by the model provider — examples include GPT-4's original September 2021 cutoff and the later cutoffs published for Claude, Gemini, and Llama releases. Cutoffs solve a practical dataset-engineering problem and a reproducibility problem: training runs are long, the open web changes constantly, and a fixed cutoff makes evaluations comparable across runs. They also create the obvious limitation that the model cannot answer questions about subsequent events without external help. Standard workarounds include RAG (Retrieval-Augmented Generation): How LLMs Access External Knowledge, live-search tools, and continual fine-tuning of smaller adapters. A reported cutoff is a single date, but a model's actual knowledge is not uniformly current up to it. Coverage of any given topic depends on how densely that topic appeared in the training data near the cutoff — see effective cutoff for the gap between the advertised date and the date the model behaves as if it knows. Cutoffs also do not imply any awareness of the present: a model with a 2024 cutoff has no built-in sense that it is now any later than 2024 unless the system prompt or a tool tells it so.