Retrieval-augmented generation (RAG) is moving from prototypes to production, but many teams overlook critical data management concerns. This post highlights the need to bake permissions and versioning directly into your vector database schema. Without these, you risk leaking sensitive data or serving stale embeddings to users. The author suggests treating vector stores like any other production database: define access policies, track data lineage, and version your embeddings. This approach simplifies auditing and rollback, making your RAG pipeline more robust. For engineers building AI features, this is a timely reminder that infrastructure discipline still matters in the age of LLMs.
A practical look at adding access control and versioning to vector databases for reliable RAG systems.