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This post explains how Dropbox Dash trains its search ranking model by combining small-scale human labeling with LLM-generated relevance judgments to produce training data at scale.
•Dash follows a RAG pattern where enterprise search retrieves candidate documents before an LLM generates answers, making search ranking quality critical to overall response quality
•The ranking model uses XGBoost trained on query-document pairs annotated with 1-5 relevance scores, where higher scores indicate closer alignment with user intent
•Human labeling is expensive and hard to scale, while LLMs offer cheaper and more consistent relevance judgments across large multilingual datasets
•A small human-labeled dataset is used to tune LLM prompts and validate quality thresholds before deploying the LLM to generate hundreds of thousands to millions of training labels
•LLM accuracy is measured via mean squared error against human judgments, and document sampling prioritizes cases where LLM predictions di
This summary was automatically generated by AI based on the original article and may not be fully accurate.
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