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What retrieval quality measures
Retrieval quality describes the evidence returned for a query, before judging how a language model uses it. A retriever can miss the document that answers a question, return useful evidence alongside a large amount of noise, or find the right material but rank it too low for downstream use.
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Those are different failure modes, so a single score is rarely enough. Evaluate three retrieval dimensions: completeness, focusedness and ordering. Treat answer faithfulness and relevance as separate measures of the response stage.
Build a test set with relevance judgments
Use questions representative of the people who will use the knowledge base and the work they need to do. For each question, identify the source documents or passages that count as relevant. These judgments let you check whether retrieval found the evidence that should have been found.
AWS distinguishes retrieval-only context relevance from context coverage, which requires ground-truth information about relevant context. Without judgments or another suitable ground truth, you can inspect returned results, but you cannot reliably measure whether the system retrieved all the evidence needed for a query. See AWS’s guide to reviewing metrics for RAG evaluations that use LLMs.
Measure completeness, focusedness and ordering
| Dimension | What to ask | Useful measures or checks |
|---|---|---|
| Completeness | Did retrieval return the relevant evidence needed to answer the question? | Context recall or context coverage; both depend on having an appropriate account of the relevant evidence. Ragas lists context recall, and AWS describes ground-truth-dependent context coverage. |
| Focusedness | How much of the retrieved context is relevant rather than noise? | Context precision or context relevance. Ragas lists context precision; AWS describes context relevance as a retrieval-only measure. |
| Ordering | Does useful evidence appear early in the ranked results? | Inspect the rank of relevant passages and consider rank-aware measures such as nDCG at a chosen cutoff. |
Ragas lists context precision and context recall among its RAG metrics, alongside faithfulness and response relevancy, which assess different parts of the system. Its available metrics documentation is useful for distinguishing these categories.
Inspect rankings and failure cases
Aggregate metrics can conceal whether a system tends to miss evidence, bury it, or return too much irrelevant context. Review individual queries: note which relevant passages were absent, where the first useful result appeared, and what irrelevant results displaced it.
Rank-aware evaluation is especially useful when downstream components consume only a limited number of top results. NIST’s summary of the TREC 2024 RAG Track reports comparisons using nDCG@20, nDCG@100 and Recall@100. These cutoffs are examples from that study, not universal settings for enterprise knowledge bases. See NIST’s summary of its relevance-assessment study.
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Use automated relevance judgments with care
Automated assessments may help scale evaluation, but their reliability should be checked in the context where they will be used. In the TREC 2024 RAG Track study, NIST reports that rankings based on UMBRELA automated assessments correlated highly with rankings based on manual assessments across 77 runs from 19 teams. The summary does not give a numeric correlation value. This result supports automated judging as a potential aid in that study setting; it does not establish that an automated judge is valid for every enterprise corpus or query distribution.
The related ACL Anthology paper, “RAGAS: Automated Evaluation of Retrieval Augmented Generation”, introduces a reference-free framework for evaluating several RAG dimensions. Its existence does not remove the need to validate evaluation judgments against the organization’s own queries and data.
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Evaluate generated answers separately
After measuring retrieval, assess the response stage: are the generated claims supported by retrieved evidence, and does the answer address the question? Ragas lists faithfulness and response relevancy for these answer-oriented concerns; AWS also describes faithfulness and citation-related metrics for evaluations that include generated responses.
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Compare system changes on the same queries
- Record a baseline. Run the representative queries against the current index and retrieval configuration, and retain the ranked results.
- Keep judgments fixed. Use the same relevance judgments for the baseline and each candidate change.
- Compare each dimension. Check completeness, focusedness and ranking rather than relying on one aggregate score.
- Review changed cases. Inspect queries where relevant evidence was newly found, missed, or pushed lower in the ranking, as well as cases where irrelevant results increased.
- Test answers afterward. Evaluate faithfulness and response relevance separately, using the retrieved evidence available to the answer stage.
This same-set comparison is a practical way to attribute changes to a retriever, index or configuration change. The cited sources describe metric dimensions and evaluation approaches; they do not prescribe a universal enterprise test protocol.
Set thresholds for your use case
The cited sources do not establish universal pass scores for context precision, recall, coverage or ranking. Set decision thresholds against the organization’s own query set, corpus and consequences of failure. A use case where missing one policy passage is especially costly may require a different balance from one where limiting irrelevant context is the main concern.
Document the test set and judgments used, the retrieval cutoffs evaluated, and the failure patterns that matter to the intended users. Reuse that baseline when comparing changes so that a score improvement can be interpreted alongside the evidence users actually need.
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