In this episode, we chat with David Jayatillake, VP of AI at Cube, about semantic layers and their crucial role in making AI work reliably with data.

We explore how semantic layers act as a bridge between raw data and business meaning, and why they're more practical than pure knowledge graphs.

David shares insights from his experience at Delphi Labs, where they achieved 100% accuracy in natural language data queries by combining semantic layers with AI, compared to just 16% accuracy with direct text-to-SQL approaches.

We discuss the challenges of building and maintaining semantic layers, the importance of proper naming and documentation, and how AI can help automate their creation.

Finally, we explore the future of semantic layers in the context of AI agents and enterprise data systems, and learn about Cube's upcoming AI-powered features for 2025.

00:00 Introduction to AI and Semantic Layers
05:09 The Evolution of Semantic Layers Before and After AI
09:48 Challenges in Implementing Semantic Layers
14:11 The Role of Semantic Layers in Data Access
18:59 The Future of Semantic Layers with AI
23:25 Comparing Text to SQL and Semantic Layer Approaches
27:40 Limitations and Constraints of Semantic Layers
30:08 Understanding LLMs and Semantic Errors
35:03 The Importance of Naming in Semantic Layers
37:07 Debugging Semantic Issues in LLMs
38:07 The Future of LLMs as Agents
41:53 Discovering Services for LLM Agents
50:34 What's Next for Cube and AI Integration

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