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Evaluate a retrieval-augmented generation (RAG) system in two stages: measure whether retrieval supplies the right evidence, then judge whether generation uses that evidence to answer well. A repeatable harness should retain per-query inputs, retrieved results, answers, evaluator versions, and failure categories so a change in aggregate scores can be traced to specific cases.
What should a RAG evaluation measure?
A RAG pipeline has two connected but distinct jobs. The retriever selects documents or chunks for a query; the generator uses the query and retrieved context to produce an answer. A weak answer may mean the evidence was never retrieved, or that the generator mishandled evidence it received. One end-to-end score cannot reliably distinguish those causes.
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Arize Phoenix’s evaluator guidance recommends debugging retrieval first and assessing generation quality afterward. Keep results for each stage separate, then inspect their relationship when diagnosing a failed case.
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When you have query-to-document relevance judgments, conventional information-retrieval (IR) metrics show different aspects of retrieval performance. They are not interchangeable: choose the metric that matches the failure you care about, and report its cutoff, dataset, relevance definition, and aggregation method.
#1 Best Overall
| Metric | Question it answers | What to report |
|---|---|---|
| Recall@k | What share of all relevant documents appeared in the first k results? | The cutoff k, the relevant-document set, and how scores are aggregated across queries. |
| Precision@k | What share of the first k results were judged relevant? | The cutoff k, the relevance rule, and the aggregation method. |
| MRR (mean reciprocal rank) | How high is the first relevant result? | The ranking cutoff, if one is used, and the set of queries included in the mean. |
| NDCG (normalized discounted cumulative gain) | How well are results ranked, accounting for position and, when available, graded relevance? | The relevance grades, cutoff k, and aggregation method. |
No relevance labels?
Without query-document relevance labels, those standard IR metrics cannot provide a ground-truth score. Phoenix describes holistic LLM-based evaluation of retrieval relevance as an alternative signal. Treat it as a different method, not as equivalent to human-reviewed relevance judgments: the evaluator’s criteria and outputs need to be understood and reviewed.
Make scores interpretable
A Recall@5 result and a Recall@10 result answer different questions, and scores based on different label definitions or query sets are not directly comparable. The Phoenix guidance defines these metrics but does not establish a universal acceptable threshold. Set targets around the application’s failure costs and validate them against reviewed examples rather than adopting an unsupported industry-wide cutoff.
Rank #2
- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
Is the answer supported by the retrieved evidence?
Assess generated answers with explicit dimensions rather than a single vague quality score. Phoenix names faithfulness, relevance, and completeness; TruLens materials describe a related triad of context relevance, groundedness, and answer relevance. The labels differ, but together they help separate poor evidence selection from unsupported claims or an answer that misses the question.
| Dimension | What to check | Typical signal of failure |
|---|---|---|
| Context relevance | Does the retrieved context contain evidence pertinent to the query? | The answer is given irrelevant or insufficient context to work from. |
| Faithfulness or groundedness | Are the answer’s claims supported by the retrieved context? | The answer invents, contradicts, or overstates what the context says. |
| Answer relevance | Does the response address the user’s question? | The response wanders away from the request despite having usable evidence. |
| Completeness | Does it cover the key points needed to answer the question? | The response omits an important part of the answer. |
Record the evaluator or rubric used for each dimension. If an LLM judge supplies scores, preserve its instructions and model/version in the evaluation record; a score without its evaluation setup is difficult to interpret across runs.
Rank #3
When answers include citations
Score citation correctness separately from general faithfulness. 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 this from citation attribution when explicit numbered markers are used. Confirm the exact behavior and API against the release you intend to use; those release notes do not establish what every later version or configuration supports.
How should you diagnose a failed case?
Classify the failure before changing the system. Phoenix’s examples include retrieval failures such as no relevant documents, partial retrieval, or the right document in the wrong chunk; generation failures include hallucination, ignored context, incomplete answers, and incorrect synthesis.
Rank #4
- Check retrieval first. Compare the retrieved IDs and context with the judged relevant documents or chunks. If needed evidence is absent, investigate retrieval, chunking, or ranking before tuning the generation prompt.
- Check evidence use. If the necessary evidence is present, inspect whether the answer follows it or introduces unsupported claims.
- Check answer quality. If the answer is grounded, assess whether it directly addresses the query and covers the required points.
- Assign a failure category. Record the category and the evidence that supports it so the team can choose an engineering action and revisit the same case after a change.
What belongs in a repeatable test harness?
Build an offline evaluation record that makes each case reproducible and explains its scores. At minimum, retain:
- A stable case ID and the query.
- Expected relevant document or chunk IDs, the relevance-label definition, and graded judgments where used.
- Retrieved IDs in rank order, the retrieval cutoff k, and the text or context supplied to generation.
- The generated answer and, where available, a reference answer or human-annotated key points.
- Per-case retrieval metrics and generation-evaluator outputs, along with evaluator, prompt, and model versions recorded by the implementation.
- The failure category and enough trace context to reproduce the pipeline run.
Build coverage without mistaking generated cases for ground truth
Phoenix demonstrates creating retrieval test pairs by generating questions that documents can answer, then evaluating retrieved IDs against relevant IDs. Synthetic questions can help bootstrap a dataset, but they are not independent human ground truth. Review them, and add real user queries and edge cases where possible. The cited guidance does not prescribe a dataset size or a statistically justified acceptance threshold.
Best Value
Compare versions on the same cases
Run the old and changed pipeline against the same evaluation cases, and preserve the relevant configuration and version details. Report retrieval and generation scores separately, alongside per-query examples and failure categories; an aggregate can conceal a serious regression in a particular kind of case. Treat this as a reproducible engineering comparison, not proof that a change will behave identically on every future query.
How should you choose evaluation tooling?
Phoenix’s evaluator guidance is a reference for IR metrics, relevance labels, and staged debugging. TruLens materials offer a complementary approach centered on context relevance, groundedness, answer relevance, and trace-oriented evaluation. These examples do not establish a complete or current comparison of evaluation frameworks.
Assess a candidate tool against the work your team needs it to do:
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- Can it assess grounding, relevance, and completeness, and expose the rubric or evaluator behind those results?
- Can it preserve per-query or per-step traces for diagnosis?
- Does it support offline evaluation against a fixed dataset, production monitoring, or both?
- What integration work, model or provider dependencies, data-handling terms, and operational costs apply to your setup?
The cited materials support these evaluation concepts and some tracing capabilities, but not a vendor-neutral account of every tool’s current pricing, deployment options, privacy terms, or compatibility. Verify those details in the relevant official documentation before choosing a tool.
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