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The four commonly used RAG evaluation metrics answer different questions: context precision and context recall probe retrieval, while faithfulness and answer relevancy probe generation. Read them together to locate what to investigate—not as interchangeable scores or proof that a system is correct. Metric names and calculations vary by framework, so identify the implementation whenever you report or compare results.
What the four metrics measure
Retrieval-augmented generation (RAG) systems retrieve material to provide context for a language model’s answer. The metrics below examine different parts of that process. DeepEval explicitly groups contextual precision and contextual recall with retriever measures, and faithfulness and answer relevancy with generator measures; Ragas also lists these metrics among its RAG evaluation options.
| Metric | Question it asks | Weak result: what to investigate | Key limitation |
|---|---|---|---|
| Context precision | Are useful context items ranked or selected ahead of irrelevant ones? | Retrieval ranking, filtering, top-K settings, chunking, or noise in the retrieved set. | Definitions differ; some implementations require an expected answer. |
| Context recall | Did retrieval include the information needed to answer? | Missing documents, query formulation, chunking, index coverage, or retrieval depth. | Reference-based evaluation needs labelled target information. High recall does not guarantee a useful answer. |
| Faithfulness | Are the answer’s claims supported by the retrieved context? | Unsupported elaboration, generation behavior, or a mismatch between context and answer. | Support in retrieved text is not proof that the text—or the answer—is true in the real world. |
| Answer or response relevancy | Does the answer address the user’s question? | Prompt or template design and response alignment. | An on-topic answer can still be unsupported, incomplete, or wrong. |
DeepEval describes faithfulness in terms of whether answer claims are supported by the retrieval context. Ragas offers a broader catalogue of metrics and variants, so the exact meaning and required inputs depend on the chosen implementation. See DeepEval’s faithfulness documentation and the Ragas metric catalogue.
How to interpret metric combinations
Use combinations as diagnostic clues, not as guaranteed explanations. A metric pattern suggests where to inspect; examples and controlled changes are needed to confirm the cause.
#1 Best Overall
Low context precision with reasonable context recall
The retrieved set may contain the needed evidence alongside distracting or irrelevant chunks. Inspect ranking, filtering, chunking, and the number of retrieved items.
Low context recall
Check whether the required evidence appears in the retrieved set before attributing the failure to generation. Investigate query formulation, index coverage, chunk size, and retrieval depth.
High answer relevancy with low faithfulness
The response may address the question while introducing claims that the retrieved context does not support. Compare the answer’s claims with the trace and inspect generation behavior.
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High faithfulness with low answer relevancy
The answer may stay within the available evidence but fail to answer what the user asked. Inspect the prompt and how the response is constructed.
Good averages but poor user outcomes
An aggregate score can hide failures concentrated in a query type or a small set of consequential cases. Segment results by query type and inspect individual examples. These patterns are hypotheses based on the metrics’ roles, not guarantees of root cause. DeepEval’s diagnostic guidance maps answer relevancy to the prompt template, faithfulness to the generator, and contextual relevancy to factors including chunk size, top-K, and embedding model. Its RAG triad guide provides further detail.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reference-based and referenceless evaluation
A reference-based metric checks results against target information, such as a labelled expected answer. A referenceless metric can be used without that expected output, but does not thereby establish correctness.
DeepEval’s RAG triad comprises answer relevancy, faithfulness, and contextual relevancy without an expected output; its guide says contextual precision and recall require a labelled expected answer. Ragas lists multiple RAG metrics and variants, so confirm the particular metric’s definition and required inputs before comparing scores across frameworks. The foundational RAGAS paper discusses evaluation of retrieval and generation in RAG systems: RAGAS: Automated Evaluation of Retrieval Augmented Generation.
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Quick Recap
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A practical workflow for using the scores
- Define the failure you want to catch. Decide whether the concern is missing evidence, irrelevant evidence, unsupported claims, or an answer that does not respond to the question.
- Build representative test cases. Include difficult query types and known failure cases. Keep expected answers or evidence labels where feasible so retrieval coverage can be evaluated.
- Document the evaluation setup. Report metric definitions, framework and version, judge configuration, and dataset alongside scores. Without those details, comparisons are difficult to interpret.
- Inspect examples and disagreements. Review scored examples and evaluator reasons, especially when metrics point in different directions. Treat an LLM judge as an evaluator that requires validation, not as an oracle.
- Calibrate against the task. Set thresholds for the intended use and review important cases with people. A configured threshold is an implementation feature, not a universal RAG quality standard.
- Test suspected fixes deliberately. Change one component at a time and check whether the relevant examples improve. A 2026 applied study cautions that metric relevance can depend on the dataset and evaluation criterion, and recommends checking that a metric approximates the criterion being used: Evaluating RAG Metrics in Applied Contexts.
How to report a RAG evaluation clearly
- Name the framework, version, and specific metric variant rather than reporting a bare label.
- State whether the metric uses expected answers or other labelled target information.
- Describe the dataset and judge configuration so readers can understand what the score represents.
- Pair aggregate results with examples and task-specific checks; a score alone cannot establish end-to-end quality.
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