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Semantic Layers: The Missing Link Between AI and Data with David Jayatillake from Cube
Manage episode 467664372 series 3594857
Kostas Pardalis, Nitay Joffe에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Kostas Pardalis, Nitay Joffe 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
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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