Get practical answers to common questions on AI Evals, based on material taught to over 2,000 engineers and PMs.
Tired of Playing Whack-a-Mole with Your LLM App?
You’ve built a promising AI feature, but getting it to work reliably feels like a constant battle. When product metrics dip, the team panics, and developers start hacking at prompts without a clear strategy.
This reactive cycle happens because teams lack a systematic way to evaluate their AI. Generic metrics like "helpfulness" are too vague, and foundation model benchmarks don't reflect your specific application's failures.
This free email series, taught by Hamel Husain and Shreya Shankar, introduces concepts from the Analyze-Measure-Improve lifecycle. It’s a framework for building and maintaining high-quality LLM applications, based on questions from over 2,000 engineers and PMs from companies including Google, OpenAI, and Meta.
What You'll Get in This Free Email Series:
You'll receive 17 emails that deliver insights from the most common questions we get from students in our full course. The series includes two free e-books: a Consolidated LLM Evals FAQ and an Advanced RAG Optimization & Evals guide.
Throughout the series, you will get an introduction to the principles of:
- Error Analysis: Learn why you should start by analyzing failures before building complex evaluation infrastructure.
- Data Generation: Understand how to create synthetic data when you don't have production logs.
- Custom Evaluators: Learn about the trade-offs between simple, code-based checks and nuanced, LLM-as-Judge evaluators.
- Human-in-the-Loop: Discover why a single domain expert is often more effective than a committee for annotation and review.
- Evaluating Complex Pipelines: Get an overview of evaluating advanced systems, including how to approach RAG pipelines, debug multi-step agents, and handle multi-turn conversations.
This Email Series Is For:
- AI & ML Engineers who are building features but struggle with reliability and regressions.
- Technical Product Managers who need a framework to define quality, track performance, and make data-driven decisions about their AI roadmap.
- Data Scientists tasked with measuring and improving the performance of LLM pipelines.
What Students Have To Say
Here are testimonials from our students:
About the Instructors
Hamel Husain brings 25 years of ML experience from roles at Airbnb and GitHub. He has consulted with over 30 companies on building and evaluating AI products.
Shreya Shankar is a CS researcher at UC Berkeley focused on GenAI-powered data pipelines. She has authored over 10 papers on MLOps and LLMOps.