Using LLMs to amplify human labeling and improve Dash search relevance
Dropbox Dash improves AI answers through retrieval-augmented generation (RAG): enterprise search retrieves relevant company documents, and an LLM uses a small subset of them to generate grounded responses. Because ranking determines which documents reach the LLM, search relevance depends heavily on high-quality query–document labels. Dash combines a small set of human judgments with large-scale LLM-generated labels to produce training data efficiently while retaining human oversight. ## How Dash search ranking works - Dash uses a trained ranking model, such as XGBoost, rather than manually configured rules. - The model learns from query–document pairs labeled on a 1–5 relevance scale: - **5:** Closely matches the user’s intent. - **1:** Not useful enough to display. - Relevance depends on the query, user context, and timing; it is not an intrinsic property of a document. - Ranking quality is especially important because enterprises may have millions or billions of indexed documents, while only a small selection can be sent to the answer-generating LLM. ## Sources of relevance labels - Labels can come from: - User behavior, such as clicks or skipped results. - Human evaluators assigning relevance scores. - LLMs directly judging query–document relevance. - Behavioral signals are useful but often sparse, biased by existing rankings, and unevenly distributed, so they work best as a supplement. - Human evaluators can provide comprehensive judgments across result sets, but labeling is expensive, difficult to scale, and vulnerable to inconsistency. - Humans also cannot directly review sensitive or proprietary customer data in this process, and different content types—such as Slack messages, Jira tickets, and Salesforce records—require different contextual expertise. ## LLM-assisted relevance evaluation - LLMs can evaluate far larger candidate sets at lower cost and with greater consistency than human annotators. - They can operate across languages and analyze customer content within established compliance boundaries. - Their judgments still depend on the model’s quality and the clarity of the evaluation prompt. - LLM-generated labels therefore require calibration and validation before being used for model training. ## Combining human review with LLM scale - Dropbox first creates a relatively small, high-quality dataset using human evaluators and limited, non-sensitive internal data. - These human labels are used to tune LLM prompts and model parameters. - Once the LLM meets quality thresholds, it generates hundreds of thousands or millions of relevance labels. - This approach multiplies human labeling effort by roughly 100 times, enabling broader and more representative training data. - LLMs are used offline rather than directly at query time because production-time use would introduce excessive latency and context-window limitations. - The LLM acts as a teacher for smaller, faster ranking models that can serve searches at scale. ## Evaluation as the foundation - Dash follows an iterative process: measure performance, change the model or instructions, and measure again. - The article compares this to chess engines, where the quality of the evaluation function determines which possible moves are preserved or discarded. - The same principle applies to ranking: poor relevance judgments can cause useful search-result patterns to be eliminated, while accurate judgments guide the model toward better rankings. Dash’s approach uses humans for quality control and contextual grounding, then uses LLMs to expand that expertise into large-scale training data. This hybrid strategy offers a practical way to improve enterprise search relevance without exposing customer data to human reviewers or imposing LLM latency on every search.
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