Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG

We all want to replace static dashboards with conversational AI. However, studies show that "out-of-the-box" LLMs struggle with Text-to-SQL on complex enterprise schemas. The missing link isn't a better model - it's the Semantic Layer.

In this fast-paced session, we will bridge the gap between 90s BI wisdom and the GenAI era.

We will cover:
- The Context Gap: Why zero-shot prompting fails and why locking semantics in BI is a dead end for AI agents.
- The Architecture: How modern solutions bring semantics closer to the Data Warehouse, where they belong.
- Back to the Future: What we can learn from "ancient" tools like OBIEE/Cognos about governing complex data relationships.
- The "Semantic RAG" Pattern: The ultimate solution. How injecting your semantic model as context (RAG) into the LLM can help. This allows the AI to understand table relationships and business logic (like a semantic layer) but retain the flexibility to write raw SQL for ad-hoc queries.

Join this talk to learn how to make the next step towards building the backend that finally makes "Chat with Data" a reality.

- Why zero-shot prompting fails and how semantic layer can help with that. - How modern solutions bring semantics closer to the Data Warehouse. - How "Semantic RAG" Pattern can help AI to understand table relationships and business logic but retain the flexibility of writing raw SQL.