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 For example, asking a model about events after its cutoff without grounding will likely produce hallucinated or outdated answers.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For example, asking a model about events after its cutoff without grounding will likely produce hallucinated or outdated answers.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
Common questions
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
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
Context Recall
A RAG evaluation metric that measures how much of the information needed to answer a question was actually retrieved.
RAG & Retrieval
Faithfulness (RAG Metric)
A RAG evaluation metric that measures how factually consistent a generated answer is with the retrieved context.
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