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