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To tell whether retrieval is good, test it against representative questions and judged-relevant documents or passages. Check whether the right evidence appears in the retrieved results and ranks high enough to use. Then evaluate the generated answer separately: a fluent response does not prove retrieval worked, and retrieval metrics alone do not prove the full system gives useful answers.
What “good retrieval” means in a RAG system
A RAG pipeline has at least two distinct outcomes to evaluate: what the retriever returns, and what the system answers using that context. Retrieval is doing its job when the passages needed for a query are present and ranked usefully, without so much irrelevant material that it obscures the evidence. The answer stage has a different job: respond to the question using that evidence accurately and completely enough for the task.
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Keep those judgments separate. If a relevant passage never reaches the model, the answer may be impossible to produce correctly. If the passage is present but the answer misuses it, improving retrieval may not address the failure.
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| Stage | Question to ask | Useful measures or checks |
|---|---|---|
| Retrieved results | Were relevant documents or passages returned, and were the best ones near the top? | Relevance and ranking measures, judged against query-specific relevance labels. |
| Context sent to the model | Is the context focused on the question, and does it contain the evidence needed to answer? | Context relevance and context recall. In the cited Microsoft metric description, context recall uses an annotated answer as a proxy for whether needed evidence is present. |
| Generated answer | Are claims supported by the context, and does the response answer the user’s actual question? | Faithfulness or groundedness, answer relevance, completeness, and task-specific correctness. |
These dimensions are related but not interchangeable. A grounded response can still be incomplete or wrong for the user’s task. Likewise, answer relevance means the response addresses the query; it does not by itself establish factual correctness.
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Retrieval relevance and ranking
For each test query, identify which documents or passages count as relevant, then check both whether they appear and where they rank. Ranking matters because a useful passage buried below less relevant results may not make it into the context supplied to the model.
Microsoft Foundry documents one implementation that uses relevance-labeled examples and reports measures including Fidelity, NDCG, XDCG, Max Relevance, and Holes. These are names used by that evaluator, not a universal set of metrics available in every RAG framework. Microsoft’s information-retrieval guidance also notes that query type can affect which fields or ranking signals deserve weight, so choose measures that reflect the workload rather than relying on an unrelated benchmark.
Context relevance and recall
Context relevance asks whether the retrieved material is focused on the query. Context recall asks whether the context contains the evidence needed to answer. A system can score well on one and poorly on the other: narrowly relevant passages may omit a key fact, while a context containing the answer may also include distracting material.
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Faithfulness or groundedness checks whether answer claims are supported by the supplied context. Answer relevance checks whether the response addresses the query. Completeness and correctness add further checks: did the response cover the necessary parts of the task, and was it accurate? Evaluate these separately when they matter to your application rather than compressing them into one blended judgment.
RAGAS describes automated evaluation in terms of faithfulness, answer relevance, and context relevance. Its current metric documentation also lists faithfulness, answer accuracy, and context relevance. Metric names and implementations can change by tool and version, so consult the framework’s current documentation before relying on particular names or code.
Build a repeatable evaluation set
Choose queries that represent real use
Include common questions as well as cases that stress different parts of the system: ambiguous wording, multi-part questions, questions that require multiple passages, and questions whose answers are absent from the index. There is no universal test-set size or canonical query taxonomy established here; the useful set is one that reflects the ways people actually use your application.
Record relevant evidence
For each query, mark the documents or passages that should count as relevant, using a consistent relevance scheme. Start with a smaller set of carefully judged examples if labeling the full workload is impractical. Automated methods can help expand or triage labels, but validate those labels before treating their scores as reliable.
Keep enough pipeline output to diagnose failures
For each run, retain the query, retrieved passages and their ranks, the context actually sent to the model, the generated answer, and the configuration or version under test. Looking at only the final answer hides whether a failure began in retrieval, context construction, or generation.
Best Value
Run the evaluation and compare changes
- Score retrieval first. Apply relevance and ranking measures that match your workload, then inspect missed relevant passages and noisy high-ranked results.
- Score the answer independently. Assess grounding, relevance, completeness, and correctness according to the task. Do not treat a strong score in one dimension as proof that the others are strong.
- Compare configurations on the same cases. Reuse the same evaluation set when changing retrieval, ranking, context construction, or generation settings. Where feasible, change one meaningful component at a time so the effect is easier to interpret.
- Repeat model-based evaluations when variability matters. Model responses can be nondeterministic. If a single run could change your decision, compare results across repeated runs and consider ranges or distributions rather than treating one result as definitive.
- Review failures by query type. An overall average can conceal a weak category or a severe miss. Examine missed evidence, irrelevant high-ranked passages, unsupported claims, and unanswered questions individually.
Report the dimensions that matter to the application instead of relying on one headline score. Relevant comparisons include evidence coverage, ranking, irrelevant context, answer grounding, answer usefulness, stability, and operational cost. The appropriate cost tradeoff depends on the application; no universal cost threshold follows from these quality measures.
Use the failure to find the failing stage
| What you observe | Where to investigate |
|---|---|
| The relevant document or passage is missing from retrieved results. | Indexing or retrieval: check whether the needed material is indexed and whether the retrieval configuration can find it. |
| Relevant evidence is retrieved but omitted from the context sent to the model, or the context is insufficient. | Context construction: inspect how results are selected and assembled before generation. |
| The needed evidence is in the supplied context, but the response ignores or misinterprets it. | Generation and prompting: investigate how the model uses the available evidence. |
| The answer makes claims unsupported by the supplied context. | Grounding and generation: check whether the response stays within the evidence and whether unsupported claims are being detected. |
| The answer is grounded but still incomplete or incorrect for the task. | Check whether the context contains all necessary evidence, whether the evidence was interpreted correctly, and whether the evaluation criteria capture the task’s requirements. |
The last case is why retrieval quality cannot stand in for end-to-end quality. Use the retrieved passages and the answer together to locate the problem, then rerun the same cases after making a targeted change.
Quick Recap
How to interpret metrics without overclaiming
- A retrieval score is not a product verdict. It says something about the measured search behavior against the chosen judgments, not whether every answer is useful.
- A fluent answer is not evidence of successful retrieval. Inspect the passages and ranks to confirm that the system found the right sources.
- One metric cannot answer every question. Coverage, ranking, context noise, grounding, completeness, and correctness describe different failure modes.
- Model-generated evaluation scores are diagnostic signals. Use them to identify patterns and compare runs, but do not treat them as universal proof of quality or as a substitute for inspecting important failures.
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