A framework for assessing retrieval-augmented generation across retrieval and answer-generation quality.
Sign in to saveRagas introduces reference-free evaluation methods for RAG pipelines. It considers whether retrieved passages are relevant, whether generated answers use those passages faithfully, and the quality of the answers themselves. Automated metrics help compare changes, but they should be interpreted in the context of the application and evaluation data.
Research paper first submitted to arXiv on 2023-09-26.
Useful for designing evaluation of document assistants. Test retrieval and generation separately so an answer failure can be traced to the right stage. Catalog note: this is a source-verified reference entry, not a measured current-trend signal. No search-volume or growth claim is made.
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