How would you evaluate retrieval separately from generation in a RAG system?

Builds a query-and-relevance dataset and separates retrieval recall, ranking quality, context quality, and generated-answer quality.

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TL;DR

I create representative queries with relevant document or chunk judgments, including valid no-answer cases. Retrieval metrics such as recall at k, precision at k, mean reciprocal rank, or normalized discounted cumulative gain measure whether useful evidence was found and ordered. Generation is then evaluated on answer correctness, support, completeness, and citation behavior using a fixed retrieved context.

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Practice focus

Evaluation decomposition, labeled evidence, slices, regressions, and metric limits.

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Answer path

Core answer first, then checkpoints toward a stronger answer.

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Core answer
Start here. This is the direct spoken answer to practice first.
Overview
End-to-end answer scores alone cannot tell whether retrieval or generation caused a failure.
I create representative queries with relevant document or chunk judgments, including valid no-answer cases. Retrieval metrics such as recall at k, precision at k, mean reciprocal rank, or normalized discounted cumulative gain measure whether useful evidence was found and ordered. Generation is then evaluated on answer correctness, support, completeness, and citation behavior using a fixed retrieved context.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
The dataset includes exact identifiers, paraphrases, multi-part questions, stale documents, permission boundaries, rare topics, and ambiguous requests. I inspect results by slice because an aggregate score can hide a severe regression for one language or document type. Human judgments remain the reference for difficult relevance and support decisions, while automated graders are calibrated and monitored against them.

Review signals

Use these to catch weak phrasing, shallow reasoning, and risky claims.

Common mistakesCandidateCommon mistakesMistakes that weaken the answer or signal shallow understanding.Open details

Details

Scoring only the final generated answer.
Building a dataset with no no-answer or permission cases.
Comparing retrievers against different corpus versions.

How would you evaluate retrieval separately from generation in a RAG system?

Practice focus

Answer path

Overview

Review signals

Follow-up prompts

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