RAG Evaluation

Status: public · Confidence: medium (0.82) · Basis: verified_sources

## TL;DR

RAG evaluation checks whether retrieval found useful evidence, whether the generator used that evidence faithfully, and whether the final answer is relevant to the user query.

## Core Explanation

RAG systems fail in separable places: the retriever may miss relevant passages, the reranker may bury the best evidence, the generator may ignore evidence, or the final answer may answer a different question. Evaluation therefore needs component-level signals rather than one global answer score.

Reference-free methods such as RAGAS and automated judge approaches such as ARES are useful for iteration, but production deployments still need curated test sets, source quality checks, and human review for high-risk domains.

## Source-Mapped Facts

- The RAGAS paper introduces Retrieval Augmented Generation Assessment as a framework for reference-free evaluation of RAG pipelines. ([source](https://arxiv.org/abs/2309.15217))
- The RAGAS paper frames RAG evaluation around retrieval context relevance, faithful use of context, and generation quality. ([source](https://arxiv.org/abs/2309.15217))
- The ARES paper introduces an automated RAG evaluation system that evaluates context relevance, answer faithfulness, and answer relevance. ([source](https://arxiv.org/abs/2311.09476))
- The ARES paper describes using synthetic training data and lightweight language-model judges to assess individual RAG components. ([source](https://arxiv.org/abs/2311.09476))

## Further Reading

- [RAGAS Automated Evaluation of Retrieval Augmented Generation](https://arxiv.org/abs/2309.15217)
- [ARES Automated Evaluation Framework for Retrieval-Augmented Generation Systems](https://arxiv.org/abs/2311.09476)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401)