RAG vs. Semantic Layer: Why AI Needs Deterministic Governance
Half the market is arguing about whether RAG or a semantic layer is the right foundation for enterprise AI. They are not competing. They answer different questions, and most teams need both. Two shapes of question Every question an agent receives breaks into one of two forms: "What did we say about X?" — lives in contracts, policies, tickets, docs. Unstructured. RAG was built for this. "What is…
Enterprise AI adoption is divided between two approaches: RAG for handling unstructured data and a semantic layer for managing structured, governed information. RAG excels at retrieving relevant prose from documents, contracts, policies, and other unstructured sources. The semantic layer, on the other hand, specializes in resolving definitions, performing joins, and maintaining current state metrics.
Despite the debate between the two, most teams require both to function optimally. The crux of the issue lies in the fact that RAG is incapable of handling aggregation, mathematical operations, and current state data, while the semantic layer's permissions are only as reliable as the retrieval index that assembles sensitive objects.
The missing piece in most architectures is the integration of intent, context resolution, constrained planning, and governed execution. When an agent is pointed at raw tables, even the best models score poorly on real enterprise data. However, when the same model is provided with compiled, governed context, its performance significantly improves.
For a comprehensive breakdown of the division of labor, the reasons why hybrid architectures are superior, and how compile-time governance can bridge the gap, refer to the article: "RAG vs. Semantic Layer: Why AI Needs Deterministic Governance."
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
