6 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.
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.
Dropbox has acquired AI startup Mobius Labs and is integrating their multimodal AI models, called Aana, into Dropbox Dash to enable deeper understanding of rich media content.
This post outlines Dropbox's systematic evaluation framework for conversational AI, developed while building Dropbox Dash.
