Context Engineering for RAG Question Parsing: From a Raw Question to Typed Fields That Steer Retrieval and Generation

This article discusses a method for parsing raw user questions into structured, typed fields to improve retrieval-augmented generation (RAG) systems. By breaking down a messy input into four distinct components, each tailored for specific downstream calls, the approach enhances both retrieval accuracy and generation quality. It emphasizes the role of context engineering in steering RAG pipelines for enterprise document intelligence.