What does it take to transform a brilliant AI model from a research paper into a product customers can rely on? We're joined by Elizabeth Lingg, Director of Applied Research at Contextual AI (the team behind RAG), to explore the immense challenge of bridging the gap between the lab and the real world. Drawing on her impressive career at Microsoft, Apple, and in the startup scene, Elizabeth details her journey from academic researcher to an industry leader shipping production AI. 

Elizabeth shares her expert approach to measuring AI impact, emphasizing the need to correlate "inner loop" metrics like accuracy with "outer loop" metrics like customer satisfaction and the crucial "vibe check." Learn why specialized, grounded AI is essential for the enterprise and how using multiple, diverse metrics is the key to avoiding model bias and sycophancy. She provides a framework for how research and engineering teams can collaborate effectively to turn innovative ideas into robust products. 

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• Register now: Closing the AI gap: Exceeding executive expectations for AI productivity

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• Learn more about Contextual AI: Contextual.ai Website
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Referenced in today's show:

• Throwing AI at Developers Won’t Fix Their Problems
• Why language models hallucinate
• i ran Claude in a loop for three months, and it created a genz programming language called cursed

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