8 articles
Dropbox engineering shares how they used DSPy to optimize their LLM-based relevance judge for Dash, achieving significant cost and quality improvements.
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.
This article explores low-bit inference techniques that make large AI models faster and more cost-efficient to serve in production.
Josh Clemm, VP of Engineering at Dropbox, explains how Dash uses knowledge graphs, MCP, and DSPy to build a universal work search and AI assistant.
Dropbox Dash built a custom hybrid feature store to power real-time AI ranking across tens of thousands of work documents.
Dropbox's 2025 Camp Dropbox Intern Program welcomed 43 interns from 27 colleges across multiple countries for a 12-week program focused on meaningful engineering contributions.
Dropbox Dash evolved from a traditional RAG-based enterprise search into an agentic AI system, requiring a new discipline called context engineering to manage what information models receive.
This post outlines Dropbox's systematic evaluation framework for conversational AI, developed while building Dropbox Dash.
