Results from the Enterprise RAG Challenge, comparing 110 experiments from 43 teams on building Retrieval-Augmented Generation (RAG) systems. The challenge required AI solutions to automatically answer 100 complex queries across 100 large annual reports (one was over 1000 pages), including cross-document reasoning.
Teams detailed their architectures, methods, and lessons learned - expand rows in the table for insights into each approach.
Feedback welcome!
PS: There are even a few fully-local solutions in the leaderboard. Hopefully in the third round we'll have even more.
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Results from the Enterprise RAG Challenge, comparing 110 experiments from 43 teams on building Retrieval-Augmented Generation (RAG) systems. The challenge required AI solutions to automatically answer 100 complex queries across 100 large annual reports (one was over 1000 pages), including cross-document reasoning.
Teams detailed their architectures, methods, and lessons learned - expand rows in the table for insights into each approach.
Feedback welcome!
PS: There are even a few fully-local solutions in the leaderboard. Hopefully in the third round we'll have even more.