Making GenAI Useful

Lessons from Research and Deployment

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Summary

In this session, you’ll explore how AI products evolve from raw model outputs to real-world tools that drive value in production. We’ll break down how foundation models are refined after training, where emerging API capabilities are opening new doors for developers, and what separates successful GenAI apps from those that fall short.


Hosted by Aditya Challapally, with insights from Michelle Pokrass (Post-Training Research Manager at OpenAI) and Stanford professor Chris Potts, this conversation reveals what it really takes to move from cutting-edge models to AI applications that are reliable, effective, and built to last.



  • The most common blindspots managers struggle with while leading remote teams
  • The most common blindspots managers struggle with while leading remote teams
  • The most common blindspots managers struggle with while leading remote teams
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Speakers

Michelle Pokrass

Michelle Pokrass is a post-training research lead at OpenAI, where she oversees efforts to align models to the needs of power users and developers for technical, high-demand use cases across ChatGPT and the API. She works at the intersection of research and product, working to develop useful functionality like Structured Outputs in the OpenAI API. Michelle played a key role in the development of the GPT-4.1 model family, which was designed for developers and delivers significant improvements in coding, instruction-following, and long-context performance. Prior to OpenAI, Michelle worked on platforms at Clubhouse and Coinbase. She earned her Bachelor of Computer Science from the University of Waterloo.

Aditya Challapally

Aditya Challapally is a machine learning engineer and product lead. He currently works at Microsoft where he helps build GenAI products that are used by millions globally. Prior to Microsoft, Challapally led key AI initiatives at McKinsey & Company and Uber. He is an advisor to several Silicon Valley startups and Fortune500 companies, helping them execute on GenAI. His expertise extends to academia, as his book, The Product Management Interview Handbook, is used in MS&E 265: Intro to Product Management at Stanford University and in universities around the world. Challapally lectures on product management and AI at Stanford School of Engineering and Stanford Online. 

Christopher Potts

Christopher Potts is a Professor of Linguistics and, by courtesy, of Computer Science, and Director of the Center for the Study of Language and Information (CSLI) at Stanford. In his research, he develops computational models of linguistic reasoning, emotional expression, and dialogue. He is the author of the 2005 book The Logic of Conventional Implicatures as well as numerous scholarly papers in linguistics and Natural Language Processing.

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