Hamel Husain

Hamel Husain, an AI product educator and consultant, and Shreya Shankar, a researcher and product expert, share their insights on AI evals. They explain why evals are essential for AI product builders, delve into error analysis techniques, and discuss the balance between code-based evaluations and LLM judges. Listeners will learn about practical tips for implementing evals with minimal time investment and common pitfalls to avoid. The duo also highlights the importance of systematic measurement in enhancing AI product effectiveness.

Hamel Husain, an ML engineer and AI consultant, discusses how to enhance AI product quality through systematic error analysis and prompt engineering. He emphasizes the importance of examining real user interactions to identify common failures. Hamel introduces a step-by-step error analysis framework and advocates for binary evaluations over arbitrary quality scores. He also outlines techniques for building custom annotation systems and prioritizing fixes based on data, ultimately guiding product teams toward more effective AI solutions.

Hamel Husain, a machine learning engineer and evals expert, discusses the pitfalls of AI evaluations and how to adopt a data-centric approach for reliable results. He outlines ten critical mistakes teams make, debunking ineffective metrics like 'hallucination scores' in favor of tailored analytics. Hamel shares a workflow for effective error analysis, including involving domain experts wisely and avoiding hasty automation. Bryan Bischoff joins as a guest to introduce the 'Failure as a Funnel' concept, emphasizing focused debugging for complex AI systems.

Hamel Husain, an expert in AI evaluation methods, shares his vast experience training PMs and engineers at top tech firms. He breaks down how to effectively analyze real production traces, emphasizing the power of binary pass/fail ratings over complex scoring systems. Hamel explains common pitfalls in evaluation metrics and introduces practical tools for continuous monitoring. Listeners gain insights into building simple annotation tools and the importance of grounding evaluations in real problems to drive meaningful improvements.

Hamel Husain, a machine learning engineer with over 25 years of experience at GitHub and Airbnb, dives deep into the intricacies of debugging AI products. He shares insights from his work on forecasting Airbnb guest growth, highlighting challenges like data leakage. The conversation uncovers techniques for error analysis in machine learning, the importance of synthetic data, and the pitfalls of AI-generated outputs like hallucinations. Hamel emphasizes the need for systematic improvement and presents practical tips for enhancing AI evaluations.