RAG vs. Semantic Layer: Why AI Needs Deterministic Governance

1. 1Determine the use case for RAG or semantic layers in your AI project
2. 2Evaluate the trade-offs between raw data accuracy (40%) and governed data accuracy (85-100%)
3. 3Configure a semantic layer to compile governed SQL for high-stakes applications
4. 4Apply RAG to read documents and extract insights for exploratory data analysis
5. 5Compare the results of RAG and semantic layers to determine the best approach for your specific use case

Data scientists and AI engineers can benefit from understanding the differences between RAG and semantic layers to improve the accuracy of their AI models. This knowledge can help them make informed decisions about when to use each approach and how to combine them for optimal results.

💡 RAG and semantic layers serve different purposes in AI data governance, and combining them can lead to significant improvements in accuracy

🚀 Improve AI accuracy from 40% to 85-100% by choosing the right data governance approach: RAG, semantic layers, or both? 🤔