Moving an enterprise RAG system from a successful demo to production introduces problems that are easy to miss: incomplete retrieval, conflicting or stale data, unreliable model outputs, evaluation gaps, and unclear boundaries between AI recommendations and human decisions. Drawing from experience building GenAI and vector search infrastructure for enterprise customers, Sidharth Gopakumar will share a practical framework for diagnosing these failure modes, improving retrieval and evaluation, and designing appropriate human oversight. Attendees will leave with practical patterns for building RAG systems that are more reliable, measurable, and useful in real enterprise workflows.