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.

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Spis treści

LLM Evaluations for Product Managers. A practical guide to measuring, improving, and delivering value with LLMs eBook -- spis treści

• 1. The New Frontier: Why LLM Evaluation is Now a Core PM Skill
• 2. The PM's Evaluation Toolkit: Essential Concepts and Strategic Frameworks
• 3. Choosing Your Evaluation Strategy: Automated, Human-in-the-Loop, and Hybrid Approaches
• 4. Building Evaluation Datasets That Predict Real-World Success
• 5. Human-in-the-Loop Evaluation: When and How to Scale Human Judgment
• 6. Automated Evaluation at Scale: LLM-as-a-Judge and Beyond
• 7. Evaluation Infrastructure: Building vs. Buying for Product Success
• 8. Rapid Product Discovery: Lean Evaluation for Fast Iteration
• 9. Measuring Product-Model Fit: When Your LLM Delivers User Value
• 10. Evaluation-Driven Feature Prioritization and Roadmap Planning
• 11. Safety and Responsible AI: Evaluation for Trust and Compliance
• 12. Evaluating Complex Systems: RAG, Agents, and Multi-Turn Experiences
• 13. Model Evolution and A/B Testing: Managing LLM Transitions
• 14. Building Evaluation-Driven Product Teams and Culture
• 15. Scaling Evaluation Practices: From Team to Organization
• 16. Communicating Evaluation Results: From Data to Strategic Decisions
• 17. Future-Proofing Your AI Products: Adaptive Evaluation for Emerging Technologies