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Evaluate retrieval separately from answer generation. Build a versioned test set from real questions, label the passages that should support each answer, include unanswerable and ambiguous queries, and measure whether relevant evidence appears high in the ranking without excessive noise. Use precision, recall or coverage, first-relevant rank, and graded ranking metrics for retrieval. Then run a second evaluation of the generated answer for groundedness, completeness, relevance, correctness, and citation quality. Inspect failures by layer rather than relying on one average score: a fluent answer can be produced from incomplete context, while a correct passage can be retrieved and still ignored by the model.
What does “the right context” mean in a retrieval-augmented AI system?
The right context is the smallest useful set of passages that contains the evidence needed to answer a particular query. A strong retriever places those passages near the top of its results, retrieves enough separate evidence to cover every required part of the answer, and limits unrelated text that can distract the language model.
This definition has three dimensions:
- Relevance: the returned passage addresses the user’s question, not merely a neighboring topic or matching keyword.
- Coverage: the retrieved set contains all material facts needed for a complete answer.
- Ranking and efficiency: the most useful evidence appears early, within the top-K context window, without unnecessary documents that increase latency, token usage, or contradiction risk.
Retrieval quality is not the same as answer quality. An answer may sound convincing even though the retriever omitted a decisive policy clause. Conversely, the correct passage may be present while prompting, context ordering, or synthesis errors cause the model to ignore it.
How should I build a retrieval evaluation set?
Use a representative, versioned query set with explicit evidence labels and answer requirements. Do not evaluate only on hand-picked questions that the system already handles well.
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- Sample real user questions. Include different intents, entities, languages, document types, and difficulty levels. Preserve the wording users actually use, including short, vague, and misspelled queries where those occur in production.
- Define the expected evidence. Record the relevant document or passage IDs for each query. Where practical, assign graded relevance such as highly relevant, partially relevant, or irrelevant rather than a simple yes/no label.
- Record an answer or evidence checklist. List the facts that a complete answer must contain. This supports context-coverage tests and later answer-completeness checks.
- Add negative and unanswerable cases. Include out-of-scope questions, queries for information absent from the corpus, and ambiguous questions that should trigger clarification. Microsoft’s Architecture Center explicitly advises testing both positive and negative examples.
- Freeze the corpus and configuration. Version the documents, chunking rules, embedding model, lexical or vector search settings, metadata filters, reranker, and top-K value. Reuse the same frozen set after every material change so score differences are attributable.
- Stratify the set. Tag each query by intent, language, document type, freshness, difficulty, and answerability. These slices reveal regressions that a single overall average hides.
Labels should reflect what users need, not only what is easiest to annotate. If several passages are interchangeable evidence, mark all acceptable items. If a question requires facts from multiple documents, label the complete evidence set rather than only the first article that mentions the topic.
Which metrics show whether retrieval is good?
No single metric captures retrieval quality. Use complementary measures that expose noise, missing evidence, ranking position, and graded usefulness.
| Metric | What it measures | What a weak score indicates | Data required |
|---|---|---|---|
| Precision@K | Share of the top-K results judged relevant | Noise or over-retrieval; irrelevant chunks consume context | Relevance labels |
| Recall@K | Share of all known relevant items found within K results | Important evidence is missing from the supplied context | Complete relevant-item labels |
| MRR | Average reciprocal rank of the first relevant result | The first useful passage appears too far down the list | At least one relevant-item label per answerable query |
| nDCG@K | Graded ranking quality, rewarding highly relevant passages near the top | Strong evidence is ranked below merely related text | Graded relevance labels |
| Context relevance | Whether returned chunks address the query, often assessed by a label-free or language-model judge | Retrieved text is topically off target even when lexical overlap exists | Judge rubric or human labels |
| Context coverage | How much ground-truth information appears in the retrieved text | The set omits facts needed for a complete answer | Ground-truth answer or evidence checklist |
For a query with a known relevant set, precision@K is the number of relevant results in the first K divided by K. Recall@K is the number of relevant results found in the first K divided by the total number of relevant items. These metrics answer different questions: precision asks whether the context is clean; recall asks whether the evidence is complete.
MRR is useful when one passage normally contains the answer and early placement matters. nDCG is preferable when relevance has degrees—for example, an official current policy may be highly relevant, an old policy partially relevant, and a glossary definition only marginally useful.
Microsoft Foundry’s document-retrieval evaluator reports additional ranking signals, including Fidelity, nDCG, XDCG, Max Relevance, and Holes. Treat these as diagnostic views rather than universal pass/fail standards. There is no published, general-purpose pass-rate benchmark that applies to every corpus, language, or query mix.
How do I choose K and test context size?
Evaluate several top-K values instead of assuming that more retrieved chunks are better. Increasing K can improve recall while reducing precision, increasing latency and token cost, and exposing the model to contradictory or stale text.
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- Run the same query set at several K values, such as the values used by your production configuration and one or two higher and lower settings.
- Plot precision@K, recall@K or coverage, MRR, latency, and input-token volume together.
- Inspect queries where a higher K first adds the missing evidence and then begins adding noise.
- Choose the smallest K that reliably covers required evidence for each important query slice. If different intents need very different K values, use intent-aware retrieval or a reranking stage rather than one unnecessarily large global value.
Measure the context actually sent to the model after deduplication, filtering, and truncation. A retriever can score well at K while later limits remove the passage that carried the answer.
How can I tell whether retrieval failed or the language model failed?
Use a layer-by-layer diagnosis. First determine whether the needed evidence was present in the retrieved context; only then judge how the model used it.
Retrieval failure: the evidence is absent
If the relevant passage is not in the returned set, the generation model could not reliably ground its answer in that evidence. Common causes include poor chunk boundaries, missing documents, inadequate query formulation, restrictive filters, an unsuitable embedding model, or a top-K that is too small.
Generation failure: the evidence is present but unused
If the context contains the required passage and the answer is still wrong, inspect the prompt, context ordering, truncation, synthesis logic, and citation instructions. The model may privilege an earlier conflicting chunk, follow an instruction embedded in retrieved text, or answer from prior knowledge instead of the supplied evidence.
Mixed failure: partial evidence and partial answer
Many errors involve both layers. A retriever may return one of three required policy conditions, and the model may then omit the other two. Record which facts were retrieved and which appeared in the answer so the remedy is not guessed.
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Which answer metrics should I run after retrieval metrics?
Once retrieval has been scored, evaluate the complete retrieve-and-generate path with measures that reflect what the user sees.
- Groundedness or faithfulness: whether claims in the response are supported by the retrieved context. Microsoft describes groundedness as the response being based completely on the supplied context.
- Correctness: whether the answer is factually right against a reference answer or expert judgment.
- Completeness: whether it covers every required fact in the evidence checklist.
- Relevance and helpfulness: whether it answers the asked question directly at the appropriate level of detail.
- Citation precision: whether cited passages actually support the associated claims.
- Citation coverage: whether important externally verifiable claims have supporting citations.
- Logical coherence: whether the response is internally consistent and follows the requested structure.
Amazon Bedrock’s retrieve-and-generate evaluation criteria include correctness, completeness, helpfulness, logical coherence, faithfulness, citation precision, and citation coverage. Use human review for high-impact domains and calibrate any language-model judge against expert-labeled examples.
Groundedness and completeness represent different failure directions. A short answer can be perfectly grounded yet omit a required exception. A comprehensive-looking answer can cover every topic while adding unsupported claims. Report both rather than allowing one to stand in for the other.
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Metric combinations point toward different engineering actions, but each diagnosis should be confirmed with concrete failure examples and a controlled change.
Low precision with acceptable recall
The system finds the evidence but floods the context with irrelevant chunks. Try better query rewriting, metadata filters, improved chunking, reranking, deduplication, or a lower top-K. Check whether near-duplicate chunks are inflating the noise.
High precision with low recall or coverage
The returned items are relevant but incomplete. Review chunk boundaries, indexing gaps, query expansion, hybrid lexical-plus-vector retrieval, and top-K. Confirm that the missing document is actually present and current in the corpus.
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Good retrieval with poor groundedness
The evidence is available, but the model is not using it faithfully. Inspect prompt wording, context order, delimiters, truncation, answer-synthesis code, and citation rules. Test whether removing irrelevant context improves adherence.
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Good groundedness with poor completeness
The answer stays within the retrieved evidence but does not cover all required facts. Verify multi-passage coverage, evidence checklists, and the model’s instructions to address every sub-question.
Good aggregate scores with poor user outcomes
Review query segmentation, hidden intents, stale documents, and label quality. An average can look healthy while a small but important slice—such as a particular language, document type, or safety-critical intent—fails consistently.
How should retrieval results be reported?
Publish a scorecard that makes every result reproducible and sliceable.
- Corpus and document version, including freshness date.
- Query-set version, sample counts, answerability labels, and relevance or evidence annotations.
- Embedding, lexical-search, vector-index, reranker, filter, chunking, and top-K settings.
- Precision@K, recall@K or coverage, MRR, nDCG, context relevance, latency, and token volume.
- Answer groundedness, correctness, completeness, relevance, and citation metrics for the end-to-end path.
- Separate results for positive, negative, out-of-scope, and ambiguous queries.
- Breakdowns by intent, language, document type, freshness, and difficulty.
- Representative successes and failures, with the retrieved ranks and missing or unsupported facts.
Language-model outputs are nondeterministic. Microsoft Azure Architecture Center recommends target ranges rather than one exact target and advises recording hyperparameters and results at both granular search and macro end-to-end levels. When sample sizes allow, use confidence intervals or repeated runs so a small score change is not mistaken for a real improvement.
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Rerun the fixed regression set after every material change to documents, chunking, embeddings, index construction, filters, reranking, prompts, models, or top-K. Also add newly observed production questions and failures to a growing challenge set, then periodically review whether labels still reflect current user needs.
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Keep the original benchmark stable so long-term comparisons remain meaningful, and maintain a separate evolving set for emerging topics and newly added documents. Re-evaluate when the corpus changes because freshness can alter both relevance labels and the correct answer.
What should I compare when choosing between retrieval systems?
Compare systems on the same corpus, labels, query slices, and operating constraints. A higher recall system is not automatically better if it doubles latency and overwhelms the model with irrelevant text.
| Comparison area | Questions to ask |
|---|---|
| Evidence quality | Which system has better precision, recall or coverage, MRR, and graded ranking quality at the production K? |
| Negative-query behavior | Does it avoid confident retrieval for out-of-scope or unanswerable questions? |
| Robustness | Does performance hold across languages, intents, document types, freshness levels, and difficulty slices? |
| Operational cost | What are median and tail latency, indexing time, query cost, and context-token volume? |
| Freshness | How quickly do document updates become searchable, and do old versions remain in results? |
| Evaluation burden | Which metrics require human labels, ground-truth answers, or a calibrated language-model judge? |
| End-to-end quality | After generation, which system produces better groundedness, completeness, correctness, and citation coverage? |
Do not select a retriever from an isolated demo or a single metric. Require failure examples and slice-level results, then test the candidate under the same production constraints it will face.
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What is a good precision or recall target for RAG retrieval?
There is no universal target that applies across domains. Set ranges from your own user-critical query slices, define the cost of a missed passage versus extra context, and validate the targets with expert-reviewed failures rather than adopting a benchmark number.
Can an LLM judge retrieval quality without labeled data?
A language-model judge can estimate context relevance when ground-truth labels are unavailable, but it may share the generator’s biases. Calibrate it on a human-labeled sample, monitor disagreement, and retain labeled queries for high-impact decisions.
How do I evaluate questions that have no answer in my knowledge base?
Mark them as unanswerable during test-set construction and score whether retrieval stays cautious and the final system declines or asks for clarification. Penalize confident citations to merely related passages, not just empty search results.
Should I use the same chunks for retrieval and for citations?
Not necessarily. Retrieval chunks can be optimized for ranking, while citation spans may need cleaner boundaries and enough surrounding text to support a claim. Test citation precision separately after any passage-merging or display transformation.
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