Look at Your Data: Debugging, Evaluating, and Iterating on Generative AI Systems
Everyone wants to build generative AI products that deliver real business value.
But here’s the catch: most systems fall short because teams don’t know where to start when things go wrong. Debugging, evaluation, and learning how to look at your data are essential to iterating and improving your systems effectively.
In this live-streamed fireside chat, Hugo Bowne-Anderson and Hamel Husain will explore how to:
Use error analysis to identify the biggest pain points in your LLM workflows.
Build evaluation frameworks that connect directly to your product goals.
Develop a curiosity-driven approach to looking at data and traces, so you can iterate faster.
Understand why debugging is the cornerstone of building reliable, scalable generative AI systems.
About Hamel Husain
Hamel Husain (Parlance Labs, ex-Github, Airbnb, DataRobot) has worked at the intersection of data science and AI engineering, helping teams scale LLM-powered systems through robust error analysis, evaluation, and debugging practices. His pragmatic approach emphasizes focusing on what matters most to deliver better outcomes.
Why Attend?
This is a conversation for anyone who has felt stuck trying to improve an LLM application. Whether you’re debugging multi-turn conversations, agentic systems, or building evaluation frameworks, this session will give you the practical perspectives needed to iterate and build systems that deliver.
This conversation was originally planned to be part of Hugo and Stefan Krawczyk’s Building LLM Applications for Data Scientists and Software Engineers course but we had so many requests to make it public, we’ll be live-streaming it to the world!