AI Book Club: Evals for AI Engineers

AI Builders and Learners SF Decembers book is "Evals for AI Engineers"! This is a casual-style event. Not a structured presentation on topics. Sometimes, the discussion even drifts away from the chapters, but feel free to grab the mic to help steer it back. Feel free to join the discussion even if you have not read the book chapters! :) ​Want to discuss the contents during the reading week? Join the AI Slack group: https://slack.flyte.org/ ​------------------------------------------------- About the book: • Title: Evals for AI Engineers • Authors: Shreya Shankar, Hamel Husain • Published: October 2026 O'rielly platform: https://learning.oreilly.com/library/view/evals-for-ai/9798341660717/ Chapters: 1. Introduction 2. LLMs and Evaluation Basics 3. Error Analysis 4. Collaborative Evaluation Practices 5. Implementing Automated Evaluators 6. Evaluating Multi-Turn Conversations 7. Evaluating Retrieval-Augmented Generation 8. Evaluating Tool Use and Complex Agents 9. Continuous Integration and Deployment for LLM Agents 10. Interfaces for Human Review 11. Data Analysis for Traces 12. Improving LLM Agents Book Description Stop using guesswork to find out how your AI applications are performing. Evals for AI Engineers gives you a practical framework for systematically testing, measuring, and improving AI applications, especially those built with LLMs. Written by Shreya Shankar and Hamel Husain, AI engineers with extensive experience across 35+ AI products and cutting-edge research, this book focuses on the techniques teams actually need to build reliable AI systems in production. Ideal for software engineers, technical product managers, and technical leads, this hands-on guide shows you how to build an evaluation process that improves alongside your product. • Find and prioritize failures through systematic error analysis and representative test sets • Build trustworthy evaluations using code based checks, LLM judges, human feedback, and synthetic data • Improve production systems over time by monitoring regressions and optimizing quality, latency, and cost