Production RAG

Production RAG Evaluation Guide

Evaluate retrieval quality, groundedness, citations, authorization, freshness, latency, reliability, and cost for production retrieval-augmented generation.

Published and reviewed 2026-09-11Next scheduled review: 2026-12-11PrepKloud Editorial + Technical Review

Evaluate retrieval before generation

Create labeled queries with relevant passages, authority, freshness, permission scope, and expected no-answer behavior. Measure recall at k, ranking quality, duplicate rate, filter correctness, and source-version selection. A model cannot cite evidence it never receives.

Include exact identifiers, paraphrases, multi-hop questions, conflicting sources, stale content, denied content, empty results, and hostile documents. Analyze by language, product, tenant, source type, and query difficulty.

Measure claim support and citation validity

Break answers into material claims and check whether the cited passages support them. Score correctness, completeness, citation precision, citation recall, abstention, and conflict handling. Fluency and similarity are not substitutes for evidence.

Validate citation identifiers against the authorized retrieved set after generation. Removed, denied, stale, or unrelated sources must never appear merely because the model emitted a plausible ID.

Connect quality to operations

Trace ingestion, embedding, query rewriting, filtering, lexical and vector retrieval, fusion, reranking, context assembly, generation, and citation validation. Record versions, counts, durations, status, tokens, and safe error classes.

Report stage p50/p95/p99 latency, error and timeout rates, index freshness, deletion lag, tokens, and cost per supported answer. Evaluate fallback behavior under search, model, and identity failures.

Gate releases

Compare baseline and candidate on the same versioned set. Use noncompensable authorization and severe unsupported-claim gates alongside quality, latency, and cost. Canary with fixed probes and representative traffic where permitted, then preserve the decision and rollback manifest.

Decision framework

AreaGuidance
LayerEvidence
IngestionSource ownership, version, ACL, deletion, and chunk lineage
RetrievalRecall, ranking, filtering, freshness, and duplicates
GenerationCorrectness, support, citations, abstention, and safety
OperationsLatency, errors, drift, freshness SLO, and cost

Practical checklist

  • Version corpus, chunker, embeddings, index, prompts, and models
  • Label expected evidence and abstention
  • Test authorization before retrieval
  • Validate claim-level citations
  • Run injection and stale-source cases
  • Compare cost per supported answer

First-party sources

Source status last checked 2026-09-11. Links can change after publication.

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