RAG & Retrieval
Knowledge Cutoff
The date after which a model has no training data.
In depth
The date after which a model has no training data. Information published after the knowledge cutoff is invisible to the model unless provided through context (RAG, tool use, or web search). Knowing a model's cutoff date helps you decide when to supplement it with retrieved information. For example, asking a model about events after its cutoff without grounding will likely produce hallucinated or outdated answers.
Example
For example, asking a model about events after its cutoff without grounding will likely produce hallucinated or outdated answers.
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Hands-on guides, comparisons, and tutorials that cover RAG & Retrieval.
FAQ
What is Knowledge Cutoff?
The date after which a model has no training data.
Why does Knowledge Cutoff matter for AI developers?
Knowledge Cutoff sits in the RAG & Retrieval part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Knowledge Cutoff?
Developers Digest publishes tutorials and videos that cover RAG & Retrieval topics including Knowledge Cutoff. Check the blog and YouTube channel for hands-on walkthroughs.
Related terms
RAG & Retrieval
Knowledge Base
A structured repository of information that an AI system can query to answer questions or provide context.
RAG & Retrieval
Grounding
Connecting a model's responses to verified, external data sources rather than relying solely on its training data.
RAG & Retrieval
Hybrid Search
A retrieval strategy that combines keyword-based search (BM25, TF-IDF) with semantic vector search (embeddings) to get the best of both approaches.
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