Our AI Research: How We Evaluate Semantic Search Technology

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This post details Exa's evaluation methodology for its semantic search technology, including LLM-based pure result grading and RAG grading. It also outlines a philosophy for search evaluation that prioritizes downstream task performance and granular feedback, critiquing traditional closed evaluation methods like MS Marco for their limitations regarding false negatives, scale, and distribution mismatch. The post highlights Exa's own evaluation results, showing state-of-the-art performance on various query sets, particularly on challenging Olympiad datasets.