Knowledge cutoff

The knowledge cutoff is the date up to which a large language model (LLM) was trained on its training data. Beyond this date, the model lacks knowledge of events, facts, or data occurring later.

Key implications:

• Models cannot provide accurate information about events after the cutoff date.
• Responses may be outdated or incorrect for current topics (e.g., recent news, scientific discoveries).
• Limits applicability in time-sensitive domains like finance or healthcare.

Solutions to address knowledge cutoff:

• rag (Retrieval Augmented Generation): Technique that augments LLM generation by retrieving and integrating current, relevant information from external sources during query time. This effectively bypasses the static knowledge limit of the model.
  • How it works: For a user query, RAG first retrieves recent documents from a knowledge base, then combines them with the query to guide the LLM’s response.
  • Benefit: Provides up-to-date answers without retraining the model (e.g., answering “What happened at the 2026 Olympics?” when the cutoff is 2025).

Related concepts:

• large-language-model: The foundation model that requires augmentation for current knowledge.
• Retrieval Augmented Generation: Core technique for overcoming cutoff limitations.
• Data Retrieval: Critical component of RAG systems for sourcing external information.

Further reference:

• Adam Lucek - RAG basics (video: RAG fundamentals)

2026 04 14 Adam Lucek RAG basics