RAG Evaluation Is Broken! Here's Why (And How to Fix It) - Yuval Belfer and Niv Granot
Jun 3, 2025 · 10:58
Yuval Belfer and Niv Granot of AI21 Labs argue that current RAG evaluation is broken because benchmarks rely on local questions with answers contained in single chunks, failing to reflect real-world messy data. They demonstrate that standard RAG pipelines—like those from LangChain and LlamaIndex—achieve only 5-11% accuracy on aggregative questions about 22 FIFA World Cup documents, such as 'Which team has won the most times?' Their proposed fix converts unstructured corpuses into structured SQL databases by clustering documents, inferring schemas (e.g., year, winner, top scorer), and using text-to-SQL at inference. This structured RAG approach handles counting and max/min queries that standard chunking cannot, though it requires homogeneous data and careful normalization to avoid ambiguity (e.g., West Germany vs. Germany). They caution that RAG is not one-size-fits-all and that existing benchmarks miss these critical use cases.
