AI Product Engineering

Wed Jun 24·7:00 PM UTC

Stop Letting Your LLM "Find the Issues" In Your Data

Your AI product does something dumb. How do you fix it? Most teams guess, or dump everything into an AI and trust whatever comes back, then ship fixes that don't move anything. This session is about the alternative: a hands-on way to find what's actually broken in your product, and to put AI to work on the problem without letting it fool you. You'll leave knowing how to debug your AI for real.

You'll learn from

Shreya Shankar

AI researcher; incoming CS Professor at Carnegie Mellon

Hamel Husain

ML Engineer with 20 years of experience

Fri Jul 3·6:00 PM UTC

Why Your AI Product Feels Slow Even When the Model Is Fast

A capable model can still feel sluggish in production. The lag usually lives in the inference path: how requests queue and batch, prefill versus decode, the KV cache, and whether you stream tokens. Abi does this for a living. She'll show you where the latency comes from, and patterns that make agentic systems feel faster while costing less. There's a hands-on exercise so you can see it yourself.

You'll learn from

Abi Aryan

Founder of Abide AI; author of O'Reilly's LLMOps

Hamel Husain

ML Engineer with 20 years of experience

Tue Jul 7·5:00 PM UTC

How to choose an OCR Model

OCR can start as a simple API call, and that is often the right choice. But teams hit limits when they need better structure, custom outputs, lower cost, or more control. Joe Barrow will show when to use a managed OCR API, what a manageable OCR stack looks like, and what open models make possible.

You'll learn from

Joe Barrow

Senior Research Scientist at Adobe Research

Hamel Husain

ML Engineer with 25+ years of experience

Isaac Flath

AI product engineer, 10 years of experience in AI.

Thu Jul 9·6:00 PM UTC

Scaling Late Interaction to Billions of Documents

Single-vector retrieval is cheap, but it throws away detail that matters for hard queries. Late interaction keeps more of that detail, but the production cost is large. This lesson shows how we scaled it to billions of documents while keeping latency, filtering, and index updates practical.

You'll learn from

Marek Galovic

CEO, Co-Founder @TopK. ex-Pinecone, ex-Shopify

Hamel Husain

ML Engineer with 25+ years of experience

Isaac Flath

AI product engineer, 10 years of experience in AI.

Thu Jul 9·7:00 PM UTC

Evals Your Whole Team Can Run, Not Just the Experts

Most teams have one person who really gets evals, and everything bottlenecks on them. Lucas took Hamel's open-source eval skills and turned them into something his whole team could run. His team uses evals to optimize the WhatsApp assistant Nova Escola ships to teachers across Brazil. We'll show you exactly how, so you can do it on your own product.

You'll learn from

Hamel Husain

ML Engineer with 25+ years of experience

Lucas Machado Rocha

AI Product Manager at Nova Escola

Wed Jul 15·7:00 PM UTC

The cold-start eval problem: evals for new AI products

If you have no users and no data yet it can feel like "looks fine to me" is all you've got. Hamel and Shreya show you how to build evals from day zero, so every change you ship is one you can measure.

You'll learn from

Shreya Shankar

AI researcher; incoming CS Professor at Carnegie Mellon

Hamel Husain

ML Engineer with 20 years of experience

Fri Jul 24·7:00 PM UTC

How To Use Open Models Effectively

Frontier model prices keep climbing while open models keep getting better. For many types of production work an open model is good enough, and switching can cut your bill without hurting quality. Zach has taught thousands of engineers to run these models. He will show you when open models are the right call, and how to put them to work.

You'll learn from

Zach Mueller

Head of Developer Relations at Lambda Labs (ex-Hugging Face)

Hamel Husain

ML Engineer with 20 years of experience

Tue Jul 28·12:00 AM UTC

Why We Embed Documents, and Why You Should Go Multi-Vector

Almost everyone reaches for single-vector embeddings, then hits a wall: they dilute long documents and fall apart out-of-domain. Retrieval is about picking the right tool for the moment; single-vector methods no longer fit. Ben Clavié built some of the tools that made multi-vector retrieval usable. He'll zoom out on why he reaches for these models, and the measurable impact they bring to agents.

You'll learn from

Ben Clavié

Member of Technical Staff at Mixedbread

Hamel Husain

ML Engineer with 25+ years of experience

Wed Jul 29·7:00 PM UTC

Can AI Actually Answer Your Data Questions?

Asking business questions of your data is the hottest enterprise AI use case, and one of the hardest to evaluate. Shreya Shankar & her colleagues built a benchmark that emulates real business scenarios, replicating what trips agents up: scattered systems, messy join keys, answers buried in text. She will share strategies you can use immediately in your workflows to improve performance.

You'll learn from

Shreya Shankar

AI researcher; incoming CS Professor at Carnegie Mellon

Hamel Husain

ML Engineer with 25+ years of experience