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Evaluating RAG: Faithfulness, Context Precision, Recall (EXPLAINED)

Scenario BasedRAGHard25 min read

Hard RAG interview question on evaluating rag: faithfulness, context precision, recall — architecture, trade-offs, eval, and production patterns.

TL;DR — Quick Answer

Split eval into retrieval (context precision/recall, nDCG) and generation (faithfulness, answer relevance). Use golden Q–passage labels, LLM-as-judge with human calibration, and online thumbs-down clustering. No single metric captures end-to-end RAG quality.

The Interview Question

Explain evaluating rag: faithfulness, context precision, recall. How would you design, implement, and evaluate this in a production RAG system? Discuss trade-offs and failure modes.

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