RAG Evaluate Quality

rag-evaluate-quality — measure, do not guess

Use this workflow when the user asks to evaluate retrieval, after a relevant change, or as part of an already authorized recurring evaluation. Do not infer that every session needs a benchmark or that invoking this skill creates a recurring schedule.

Workflow

1. Call get_index_stats(). The response contains a stats object. Record stats.total_documents, stats.total_chunks, stats.embedding_model, stats.embedding_dim, and stats.query_cache.hit_rate. The cache rate describes repeated-query reuse, not relevance.

2. Reuse an independently selected evaluation set, or prepare questions with known answer documents. Each case has one non-empty question and one non-empty expected path:

   [
     {"query": "authentication design", "expected_filepath": "docs/adr/0018-auth.md"},
     {"query": "retry policy", "expected_filepath": "docs/adr/0031-retries.md"}
   ]

   These are illustrative paths. Verify that expected documents exist in the actual corpus. Do not derive the expected answer from whichever source the current search happens to rank first.

3. Serialize the array as a JSON string and pass the test_cases parameter:

   import json
   
   cases = [
       {"query": "authentication design", "expected_filepath": "docs/adr/0018-auth.md"},
       {"query": "retry policy", "expected_filepath": "docs/adr/0031-retries.md"},
   ]
   evaluate_retrieval(test_cases=json.dumps(cases))

   The MCP tool returns mrr_at_5, recall_at_5, total_queries, and per_query. It does not return Precision@5. Invalid cases are rejected before searches execute.

4. Inspect individual misses and rank changes before interpreting aggregates:

   Metric	Meaning
   MRR@5	Mean reciprocal rank of the expected document; a miss contributes zero
   Recall@5	Fraction of cases whose expected document occurs in the first five results
   found_at_rank	Rank for each case, or null if the expected document was not found

   A small corpus can still be evaluated. Report the number and coverage of questions; do not infer a universal quality threshold or declare a fixed delta statistically significant. With five cases, one changed result has a large effect.

5. Compare with a previous run only after recording corpus revision, model, dimensions, query/passage prefixes, search configuration, and question set. The tool uses the server's default query settings; it does not accept hybrid_alpha, search_method, or min_score arguments. To compare those options, execute a separate controlled search workload with the same questions.

6. Investigate changes before recommending a rebuild:

   Observation	Next check
   Expected document missing	Verify file discovery/exclusions, parse errors, indexed chunks, and category
   Rank changed after new ingestion	Inspect competing hits and per-query evidence
   Poor results in a non-English corpus	Evaluate an appropriate multilingual model and its required prefixes
   Model or passage prefix changed	Follow the full model migration procedure; incremental indexing cannot convert old vectors
   Cache hit rate is zero	Check whether queries actually repeat; no hit-rate target establishes retrieval quality

7. Save an evaluation report only where the user has authorized writing. Keep evaluation questions and results outside the measured corpus unless deliberately testing their effect; indexing the answer key can contaminate later measurements.