RAG reads documents; a semantic layer compiles governed SQL. When to use each, when to stack both, and the accuracy data: 40% raw vs. 85-100% grounded.
RAG and semantic layers answer different questions. Teams need both; the gap is governance compiling intent into deterministic, auditable execution. Agents on raw tables: ~15% accuracy. With compiled governance: 99%. Retrieval was never the bottleneck.
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.
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