ModelRefs / Context Recall — AI Glossary
Context Recall — AI Glossary
A RAG evaluation metric measuring what fraction of ground-truth answer content can be attributed to the retrieved context. Requires a ground-truth answer.
Overview
Context recall (RAGAS) measures retrieval completeness: how much of the ideal answer could be derived from the retrieved chunks. Requires a ground-truth answer. Low context recall indicates the retriever missed relevant documents. Complements context precision (are retrieved documents relevant?) as a retrieval quality metric.
Reference details
| Topic | evaluation |
|---|---|
| Last reviewed | 2026-06-24 |
Related terms
Example: The metric that tells you where to look
Answer quality is poor. Context recall is 0.4 — the retriever is not returning the material the answer needs, so no prompt change will fix it. Context recall is 0.95 and quality is still poor — retrieval is fine and the generation step or the prompt is at fault. One number redirects the whole investigation.
Commonly confused with
Context recall is not context precision. Recall asks whether the needed material was retrieved; precision asks whether what was retrieved is relevant. You can have perfect recall alongside terrible precision by returning everything, which is why the two are always read together.
When to use it
Reach for it when:
- Diagnosing whether a RAG failure is retrieval or generation
- Tuning chunk size, overlap or top-k with a measurable target
- You have ground-truth answers to evaluate against
Reach for something else when:
- Without ground truth — the metric requires an ideal answer to compare with
- As an overall quality score; it measures one stage, not the product
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Frequently asked questions
What is Context Recall?
A RAG evaluation metric measuring what fraction of ground-truth answer content can be attributed to the retrieved context.
What concepts are related to Context Recall?
Closely related concepts include context precision, faithfulness, ragas.