RAG Isn't One Problem. Teams Keep Solving the Wrong One
Techstrong.ai, Wednesday, May 13th, 2026
RAG projects fail in production because teams underestimate data preparation and governance while over-focusing on retrieval models.
The article argues that RAG implementation challenges stem from treating it as a single problem when it is structurally two distinct problems: search and context. Teams typically focus heavily on the retrieval and generation layers while neglecting critical data preparation tasks like handling ambiguous tables, inconsistent schemas, and legacy content formats.
Additionally, governance and access control requirements are consistently underestimated in enterprise deployments, leading to security and compliance issues that emerge months into production. Experts recommend adopting hybrid retrieval approaches combining keyword and vector search, treating RAG as a data platform challenge first, and establishing governed semantic layers before building retrieval systems to avoid common failure modes.