Data experts recommend semantic layers to improve AI accuracy on warehouse queries

Experts recommend placing a semantic or metrics layer between data warehouses and AI tools like LLMs. This layer should feature clear model names, documented data grain and keys, and curated views instead of raw tables. For Retrieval-Augmented Generation (RAG) systems, careful chunking with rich metadata is crucial. This approach ensures definitions are explicit and data quality checks built for humans also protect AI outputs. The goal is to prevent AI agents from confidently propagating existing data errors or ambiguities.