Learning path · Production RAG · 53
RAG Evaluation
Metrics and datasets for retrieval quality and generation faithfulness—Precision@K, Recall@K, faithfulness, answer relevance.
Why it matters
- Prevents shipping RAG that reads well but cites wrong docs.
- Separates retriever regressions from generator issues.
- Feeds continuous improvement and DSPy optimization.
Key ideas
- Precision@K
- Recall@K
- Faithfulness
- Answer relevance
Top resources
- 01DocsExploding Gradients
RAGAS
Why this resource. The metric suite teams actually run on RAG.
Covers in this concept
- faithfulness
- context precision
- 02PaperLiu et al.
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
Why this resource. LLM-as-judge protocol you will meet in eval design.
Covers in this concept
- G-Eval
- rubrics
Evaluate retrieval with Precision@K (how many top hits are relevant) and Recall@K (whether gold passages appear). Evaluate generation with faithfulness (claims supported by context) and answer relevance (addresses the question). Build sets from real support tickets and doc updates. Run evals on every index rebuild, prompt change, and model route swap—aggregate scores hide catastrophic slices. Slice eval dashboards by language, product SKU, and doc vintage; aggregate faithfulness hides failures on your riskiest segments.
Updated 2026-08-09 · Full learning path