Trial DeepEval for RAG with inspectable case-level evidence

Match the metric to the diagnostic

DeepEval documents contextual relevancy and contextual recall as separate metrics. Review the exact required fields and judge configuration for the version you intend to use. An expected output can influence a recall judgment, so its provenance matters. Do not call a metric reference-free if your chosen path depends on an expected answer. Keep the package version, metric class, threshold, evaluation model, and any prompt customization with the trial record.

Evidence: Contextual Relevancy [1]Contextual Recall [2]

Author a context-missing fixture

Use a fictional support policy with one general rule and one regional exception. Create fixed contexts containing both, only the general rule, only distractors, and the right facts from an inapplicable revision. Pair each with an authored answer and expected behavior. The fixture should reveal whether the metric distinguishes irrelevant context from incomplete evidence. Product acceptance still checks the date and regional scope directly; a contextual metric does not automatically enforce every application requirement.

Inspect reasons before trusting thresholds

Run the scoring-only fixture and compare the reasons with the visible passages. Look for a missing exception that receives a favorable score because the expected output itself omitted the exception. That is a reference-quality problem rather than evidence that the application is correct. Test an intentionally unsupported statement and an answer using a valid paraphrase. Preserve evaluation errors and skipped cases so a clean aggregate cannot result from silently dropping difficult inputs.

Test integration behavior independently

After understanding the metrics on fixed snapshots, connect the application to a held-out case set. Verify that the adapter supplies the actual retrieved context and not a separately reconstructed ideal context. Make a known failing case fail the local release check and confirm that the reason and raw evidence are retained. Record retries, model calls, and the actual endpoints contacted under your data-handling policy. A framework name alone does not establish where evaluation data is processed.

Choose a bounded operational role

Use the trial record to decide whether the metric supports triage, trend monitoring, or a narrowly defined gate. Keep deterministic eligibility checks and critical business rules independent when they are directly testable. Compare changes using stable cases and evaluator configuration, and inspect paired regressions before changing a threshold. The deliverable should let another engineer reproduce one failure with the same evidence, rather than merely open a dashboard showing a favorable average. Add an adapter audit that compares exported context with the original retrieval trace byte for byte when practical. A hidden preprocessing change can otherwise turn a framework comparison into a comparison of different supplied evidence.

Common questions