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Evaluate a retrieval-augmented generation (RAG) pipeline in two stages: check whether retrieval finds the evidence, then check whether the answer uses that evidence correctly. A repeatable test harness should retain the inputs, ranked results, answer, evaluator settings, and failure category for each case. That lets you see what regressed—and where to fix it—instead of relying on one aggregate score.
Did the retriever find the right evidence?
Retrieval metrics measure how well a system finds and ranks documents or chunks for a query. When you have relevance judgments—labels identifying which results are relevant—standard information-retrieval metrics can make this stage measurable. Each metric answers a different question.
| Metric | What it tells you | What to specify when reporting it |
|---|---|---|
| Recall@k | What share of all judged-relevant items appears in the first k results? | The cutoff k, relevance definition, dataset, and aggregation. |
| Precision@k | What share of the first k results is judged relevant? | The cutoff k, relevance definition, dataset, and aggregation. |
| MRR (mean reciprocal rank) | How highly does the first relevant result appear across queries? | The relevance rule, dataset, and aggregation. |
| NDCG (normalized discounted cumulative gain) | How well are results ranked, accounting for position and, when provided, graded relevance? | The cutoff k, grading scheme, dataset, and aggregation. |
These metrics are not interchangeable. Recall emphasizes finding relevant evidence; precision emphasizes keeping the result set relevant; MRR focuses on the first relevant result; NDCG can account for relevance grades across the ranking. Phoenix’s evaluator guidance describes these metrics and their dependence on relevance labels.
No relevance labels?
Without query-document relevance judgments, you cannot calculate these standard metrics as ground-truth measures. Phoenix describes holistic LLM-based evaluation of retrieval relevance as an alternative. Treat that as a separate signal, not as equivalent to human-reviewed labels: an LLM evaluator makes its own judgments, so its rubric and outputs should be inspectable.
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Never publish a retrieval score by itself. State the cutoff, test set, definition of relevance, and aggregation method. Results calculated with different cutoffs or labeling policies are not directly comparable. The cited guidance does not set a universal acceptable score.
Is the answer supported by the retrieved evidence?
Retrieval success does not guarantee a good answer. Score generated answers against an explicit rubric that separates the main failure modes instead of compressing them into a vague quality rating.
- Faithfulness or groundedness: Are the answer’s claims supported by the retrieved context?
- Relevance: Does the answer address the user’s question?
- Completeness: Does it cover the key points needed to answer?
Phoenix guidance names faithfulness, relevance, and completeness; TruLens materials describe a related set of dimensions: context relevance, groundedness, and answer relevance. The labels differ, but together they help distinguish poor evidence selection, unsupported claims, and answers that miss the request. Record the rubric and evaluator used so that a score has a clear meaning.
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Evaluate citation correctness separately from general groundedness. TruLens 2.12 release notes describe a citation-accuracy evaluator that checks whether citations are supported by retrieved context and penalizes claims that should be cited but lack support. The notes distinguish citation accuracy from citation attribution when explicit numbered markers are used. Check the release-specific documentation for the exact API behavior before adopting it; the release notes alone do not establish that every version or configuration behaves the same way.
How should you diagnose a failed case?
Turn each poor score into a failure category that suggests an engineering action. Phoenix’s evaluator guidance distinguishes retrieval problems such as no relevant documents, partial retrieval, or the right document with the wrong chunk from generation problems such as hallucination, ignored context, incomplete answers, or incorrect synthesis.
- Check retrieval first. Determine whether the required evidence appears in the retrieved results and whether the relevant passage or chunk was selected. If it is absent, investigate retrieval rather than changing the answer prompt.
- Check faithfulness next. If the evidence is present, inspect whether the answer’s claims follow from it or whether the system ignored or contradicted the context.
- Check answer quality. Assess whether the response answers the question and includes the necessary points without incorrect synthesis.
This order follows Phoenix’s recommendation to debug retrieval before generation quality. It helps avoid prompt changes that cannot fix evidence the model never received.
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What should a repeatable test harness record?
Keep a stable evaluation set and preserve enough information to reproduce and explain each run. A practical per-query record includes:
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- Expected relevant document or chunk IDs, the definition of relevance, and graded judgments when used.
- Retrieved IDs in rank order, the evaluated cutoff k, and the retrieved text or context.
- The generated answer and, when available, a reference answer or human-annotated key points.
- Per-case retrieval metrics and generation-evaluator outputs, together with the evaluator, prompt, and model versions used by the implementation.
- A failure category and sufficient trace context to reproduce the run.
Phoenix’s guide demonstrates creating retrieval test pairs by generating questions that documents can answer, and calculating metrics from retrieved IDs and relevant IDs. Synthetic questions can help bootstrap coverage, but they are not independent human ground truth. Review them and supplement them with real user queries and edge cases where possible.
Compare pipeline changes on the same cases
Run the old and new versions against the same evaluation cases, then retain their configuration and version details. Report retrieval and generation results separately, and include per-query examples alongside aggregates. An aggregate can obscure a specific failure type, while case-level evidence helps identify which behavior changed. These practices make comparisons reproducible; they are harness-design recommendations, not a universal protocol established by the cited guidance.
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How should you choose metrics and thresholds?
Choose metrics based on the failure you need to detect. If missing relevant evidence is costly, pay close attention to recall; if irrelevant context is causing confusion, inspect precision and context relevance. Use rank-sensitive metrics when the placement of evidence matters. For generated answers, keep grounding, relevance, and completeness distinct enough to see which dimension is failing.
Set pass/fail thresholds for the application’s own failure costs, then validate them against reviewed examples. The cited materials do not establish a universal threshold, minimum evaluation-set size, or statistical significance rule. Avoid treating a score as proof of quality without checking representative cases and the consequences of errors.
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Arize Phoenix’s evaluator guidance is a reference for retrieval metrics, relevance labels, and staged debugging. TruLens materials provide a complementary framing around context relevance, groundedness, answer relevance, and trace-oriented evaluation. These examples do not amount to a current, vendor-neutral comparison of all evaluation frameworks.
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When assessing any tool for your own pipeline, check whether it can:
- Calculate conventional retrieval metrics from judged query-document pairs.
- Assess answer grounding, relevance, and completeness while exposing the rubric or evaluator.
- Preserve per-query or per-step traces that support diagnosis.
- Support the offline evaluation, production monitoring, or both that your workflow requires.
- Fit your integration, model-provider, data-handling, and operational requirements.
Features, APIs, compatibility, deployment options, privacy terms, and costs can change. Confirm those details in the relevant official documentation before selecting or integrating a tool.
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