Evaluating RAG Systems
Layered evaluation for retrieval-augmented generation: recall@k and MRR/NDCG@k, faithfulness and answer relevance, gold evaluation sets, LLM-as-judge, RAGAS-style pipelines, and hallucination detection
A deep, interview-ready treatment of RAG evaluation: why retrieval and generation must be measured separately, worked recall@k/MRR/NDCG@k arithmetic, generation metrics including faithfulness and answer relevance, how to build and version a gold evaluation set, LLM-as-judge rubric design and bias calibration, RAGAS-style automated pipelines, hallucination-detection techniques, and a worked diagnostic example that separates a retrieval problem from a generation problem.
Practice questions (5)
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Diagnosing a RAG Metrics Dashboard
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Computing Retrieval Metrics From Raw Results
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Designing a Gold Evaluation Set From Scratch
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Calibrating an LLM-as-Judge Pipeline
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Building a Hallucination-Detection Pipeline for Production
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