Build LLM products that deliver business results using practical, evaluation-driven product strategies
Key Features
Learn how to evaluate LLMs for user value, not just technical performance Apply proven frameworks and tools to shape product strategy and execution Build evaluation-first teams that adapt quickly to AI advancements
Book DescriptionBuild AI products that dont just functionthey deliver results. This book shows product managers how to drive business value with LLMs through evaluation-first decision making. Youll 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, youll 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. Youll 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, youll know how to design scalable evaluation systems, communicate results that influence stakeholders, and future-proof your AI strategy in a rapidly evolving landscape.What you will learn
Assess LLMs based on user impact, not just technical metrics Build evaluation datasets aligned to real product use cases Implement hybrid methods combining automation and human judgment Use evaluation data to guide feature prioritization and roadmaps Design infrastructure to scale evaluation practices across teams Communicate evaluation results to drive strategic decisions Adapt evaluation strategies to fast-evolving AI capabilities
Who this book is forProduct managers building AI or LLM-based features who want practical evaluation frameworks that connect models to measurable business value. Also ideal for engineering managers and AI team leads driving evaluation strategy in fast-moving AI environments. A working knowledge of product development and collaboration with technical teams is required.
