Building Dash: How RAG and AI agents help us meet the needs of businesses

This post details the evolution of Dropbox Dash, moving from a traditional RAG pipeline to an agentic AI system. It highlights the challenges of data diversity, fragmentation, and modalities in business data environments. The core technical contributions involve the application of Retrieval-Augmented Generation (RAG) and AI agents. Specific technical trade-offs discussed include latency vs. quality in retrieval systems, data freshness vs. scalability, and budget vs. user experience. The post also mentions the engineering of a minimal Python interpreter for AI agents and the use of semantic search with vector indexes versus lexical search. It emphasizes the importance of the retrieval system in setting the bounds of an LLM's knowledge and impacting latency and perceived quality.