Evals for AI Engineers: Systematically Measuring and Improving AI Applications

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