What does context recall measure in RAG eval and what ground truth does it require? · Zenaique

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Quick reference

TL;DR

Context recall measures whether retrieved chunks cover every claim in a reference answer. It needs ground truth, which makes it costlier than context precision.

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Easy to grasp

Imagine a student writing an essay using only photocopied pages a librarian handed them. Context recall asks one question: did the librarian hand over every page the student needed to write a complete, correct essay? To check this, you first need the model answer, the essay the teacher already wrote. You take each fact in that model answer and ask whether it could have come from one of the handed-over pages. If most facts trace back to the pages, recall is high, the librarian did a good job. If half the facts are missing from the pages, recall is low, the student would have to guess or leave things out. The catch: you can only run this check if a teacher already wrote the model essay. That model answer is the expensive ingredient.

Key concepts

Context recall Context precision RAG evaluation Faithfulness

Detailed answer & concept explanation

~8 min read

Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.

Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.

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Quick scan · 2–4 min

Everything important, quickly.

5 min: define context recall as reference-claim coverage, walk the claim-level computation, contrast inputs and cost against context precision, and show why recall catches gaps that faithfulness misses.

Real-world examples

Real products, models, and research that use this idea.

RAGAS computes context recall by decomposing a reference answer into claims and entailment-checking each against retrieved context with a judge like Claude Opus 4.7 or GPT-5.5.
TruLens exposes a context-relevance metric in its RAG triad that plays the precision role, paired with groundedness for the faithfulness side.
LangSmith and Arize Phoenix both wire RAGAS-style context recall into golden-set evaluation jobs run in CI before a retriever or index change ships.
Galileo and Patronus market RAG evaluation suites that separate retrieval recall from generation faithfulness so teams can localize regressions.
DeepEval ships a contextual recall metric that requires an expected_output field, making the gold-answer dependency explicit in the API.

Interviewer follow-ups

What an interviewer would ask next. Try answering before peeking at the approach.

QHow would you build a golden set to support context recall without it going stale?

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QWhy can a RAG pipeline show high faithfulness but low context recall at the same time?

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QHow would you turn a low context recall score into an actionable retriever fix?

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Don't say this

Red flags and common mistakes that signal junior thinking. Click to expand.

Most common mistake

Confusing context recall with context precision, or with faithfulness. Recall scores the retriever's coverage against a reference answer; precision scores chunk relevance without one.

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60-second cheat sheet

The night-before-the-interview bullets. Scan these on the way to the call.

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References

Primary sources. Skim if you want the original framing.

Es et al., RAGAS: Automated Evaluation of Retrieval Augmented Generation