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Dropbox Uses LLMs To Improve Search
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8.2
Dropbox engineers describe using large language models to amplify human relevance labeling for Dash search, calibrating LLM evaluators against a small human-labeled set to produce hundreds of thousands to millions of labels and amplify human effort roughly 100×. They report the method improves retrieval ranking — the bottleneck in retrieval-augmented generation — by combining automated LLM judgments with human oversight and hardest-mistake analysis.
Scoring Rationale
Strong practical impact from scalable, human-calibrated LLM labeling; slightly limited by incremental novelty over existing RAG practices.
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