RAG and Semantic Layers Serve Different Roles in Enterprise AI Architecture

A growing debate in enterprise AI pits Retrieval-Augmented Generation (RAG) against semantic layers as foundational tools, but experts argue the two are complementary rather than competing. RAG is designed to retrieve unstructured information from documents, contracts, and policies, while semantic layers resolve structured data definitions, metrics, and database joins. A key distinction lies in permissions handling: RAG indexes flatten access controls at ingest, making entitlement reconstruction at query time unreliable, whereas semantic layers compile permissions per user and per query. Without a governed semantic layer, AI agents pointed at raw data tables perform poorly on real enterprise queries, but accuracy improves dramatically when provided with compiled, governed context. A hybrid architecture that routes unstructured retrieval through RAG and structured resolution through a semantic layer is considered necessary for reliable, auditable enterprise AI systems.