Build AI products that don’t just function—they deliver results. This book shows product managers how to drive business value with LLMs through evaluation-first decision making. You’ll learn to move beyond traditional metrics and implement strategic evaluation approaches that match real user needs, drive product iteration, and support scalable success. With case studies from GitHub, Duolingo, and Notion, you’ll discover practical tools to assess model performance, optimize product-model fit, and prioritize features based on measurable outcomes. The book provides battle-tested templates, evaluation canvases, and decision trees that help you quickly translate insights into action. You’ll explore frameworks for human-in-the-loop evaluation, LLM-as-a-judge automation, and A/B testing, all within real product development workflows. Written by a seasoned AI product leader with experience across high-stakes enterprise environments, this guide bridges the gap between model performance and business impact. By the end of this book, you’ll know how to design scalable evaluation systems, communicate results that influence stakeholders, and future-proof your AI strategy in a rapidly evolving landscape.
