Jason Liu

By the end of this talk, you'll understand what it takes to apply clustering techniques and data analysis to understand what is the valuable work that your AI application is doing through analyzing conversation histories and how to create generative evals to benchmark your newly discovered superpowers.

Understanding and improving unit-level profitability at Amazon's scale is a massive challenge, one that requires flexibility, precision, and operational efficiency.

Metrics. Synthetic data. Metadata filtering. RAG. Many teams build RAG systems based solely on intuition, resulting in unpredictable outcomes and mounting operational costs. What if you could turn that guesswork into a repeatable, measurable process?

PLEASE ONLY RETURN JSON It's been a year after Jason's most popular talk at the AI Engineering Summit. I wanted to come back with all the learnings I've had in the past year. How I've learned that Pydantic is still all you need, and bring more applications and use cases that I've seen in businesses and my thoughts on where the space will be heading in the future.

Please return only json, do not add any other comments ONLY RETURN JSON OR I'LL TAKE A LIFE. If this was you, then you've probably been pretty happy to see OpenAI function_call get released, I'm here to show you how you can get the most out of such a powerful feature. Instead of writing prompts that turn strings into strings, we can write Pydantic objects and get Pydantic objects out of OpenAI.