# Review RAG and retrieval quality

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Source documentation checked: 2026-10-09 (UTC). Directory digest: `d6020b185c1948e1d2ea6265edcb6d00574ca822925016219babede247d38cdd`.

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## Review RAG and retrieval quality

Choose what to measure when improving search, embeddings, reranking and grounded answers.

Decision question: Should we change the retriever, embeddings, reranker or answer-generation step?

When to revisit: Before redesigning retrieval, after a corpus or query change, and when observed answer quality declines.

Agent research task: Propose a retrieval comparison on a fixed corpus, then a separate answer-quality check. Explain which public benchmark resembles the task and which differences need a local test.

Compare:

- corpus and query split
- embedding and reranker revision
- retrieval metric and cutoff
- answer metric and judge
- latency and failure population

Sources:

- [MTEB](https://github.com/embeddings-benchmark/mteb): Embedding evaluations across retrieval tasks, languages and modalities. The project documents evaluation code and links its leaderboard. The code licence does not settle the licences of every benchmark dataset or model.
- [BEIR](https://github.com/beir-cellar/beir): Retrieval evaluation across heterogeneous datasets, including lexical, dense and reranking methods. Use the official project documentation and dataset references. Inspect each dataset's use conditions; retrieval scores are not end-to-end answer-quality results.
- [Ragas](https://docs.ragas.io/en/stable/): Methods for evaluating retrieval, grounded answers and agent workflows on a chosen test set. Public documentation; running a model-based evaluator needs separate model access and a budget. Pin the metric implementation and judge configuration. A judge score is not an independent human finding.

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