RAG & Retrieval
Context Recall
A RAG evaluation metric that measures how much of the information needed to answer a question was actually retrieved.
In depth
A RAG evaluation metric that measures how much of the information needed to answer a question was actually retrieved. Because it is about not missing anything, it always needs a reference: Ragas breaks a reference answer into claims and scores the fraction that can be attributed to the retrieved context, or compares retrieved chunks or document IDs against a labeled set. Low context recall points to a retrieval problem, not a generation problem.
Primary source: docs.ragas.io
Example
In practice, developers reach for Context Recall when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover RAG & Retrieval.
FAQ
Common questions
What is Context Recall?
A RAG evaluation metric that measures how much of the information needed to answer a question was actually retrieved.
Why does Context Recall matter for AI developers?
Context Recall 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 Context Recall?
Developers Digest publishes tutorials and videos that cover RAG & Retrieval topics including Context Recall. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
RAG & Retrieval
Faithfulness (RAG Metric)
A RAG evaluation metric that measures how factually consistent a generated answer is with the retrieved context.
RAG & Retrieval
Chunking
The process of splitting large documents into smaller, overlapping segments for embedding and retrieval in RAG systems.
RAG & Retrieval
Chunked Prefill
A scheduling technique that splits a long prompt's prefill into equal-sized chunks and processes them over several iterations instead of in one pass.
Put this concept to work
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