Send us a text

The challenge of semantic gaps in RAG systems, where embeddings often miss crucial connections—like linking a "PTO" query to policy language on "prorated annual leave". It details Knowledge RAG design principles for higher accuracy and user satisfaction, emphasizing proven 2025 techniques. These techniques include using LLMs for query rephrasing and multi-query generation, hybrid retrieval fusion that blends semantic vectors with keyword search, and reranking results to ensure top-k precision. The goal is to design pipelines that account for semantic mismatch, rather than expecting embeddings to "understand" complex intent.

Thank you for tuning in to "Analyze Happy: Crafting Your Data Estate"!
We hope you enjoyed today’s deep dive. If you found this episode helpful, don’t forget to subscribe for more insights on building modern data estates with Microsoft technologies like Fabric, Azure Databricks, and Power Platform.

Connect with Us:

• Have a question or topic you’d like us to cover? Reach out on linkedin.com/company/dataqubi or [email protected]
• Visit our website at www.dataqubi.com or episode resources, show notes, and additional tips on data governance, AI transformation, and best practices.

Stay Ahead:
Check out the Microsoft Learn portal for free training on Azure IoT, Fabric, and more, or explore the Azure Databricks community for the latest updates. Let’s keep crafting data solutions that fit your organization’s culture and tech landscape—happy analyzing until next time!