Interview Guides
You are building an internal assistant for an enterprise search tool that answers questions over engineering standards, project documentation, support knowledge articles, and policy PDFs. The corpus contains roughly 8 million documents across mixed formats including HTML, DOCX, PDF, email exports, and scanned files with OCR noise, and many documents are long, versioned, and access-controlled. Users ask multi-hop questions such as how a design requirement changed between revisions or which procedure applies to a specific asset type, and they expect answers with citations rather than generic summaries. You have limited labeled question-answer pairs, but you do have document metadata, user permissions, and historical search logs.
What is retrieval-augmented generation, and how would you design and implement a production-ready RAG system for this enterprise data so that answers stay grounded in the source documents while handling noisy content, permissions, and evolving knowledge?
