Build Better AI by Measuring What Matters

AI products don't fail because models are weak. They fail because teams ship without knowing what is actually working, what is failing, and why.

AI Evals: Build the Habit of Measuring is a practical, example-driven guide for building reliable evaluation systems for any LLM-powered product. Instead of focusing on theory, benchmarks, or academic metrics, this book teaches a repeatable framework for discovering failures, measuring quality, validating improvements, and deploying AI with confidence.

You'll learn how to:

• Read traces and identify real failure modes
• Build datasets that uncover hidden problems
• Create meaningful evaluation metrics
• Use code-based checks, reference-based tests, and LLM-as-Judge correctly
• Evaluate RAG systems and AI agents
• Build production evaluation pipelines
• Generate synthetic and adversarial test cases
• Connect evaluation metrics to business outcomes
• Create a continuous improvement loop for AI products

Using a realistic end-to-end example throughout the book, you'll move from intuition-driven decisions to measurable quality systems that scale.

Whether you're a Product Manager, AI Engineer, Founder, Engineering Leader, or AI Practitioner, this book provides the tools, frameworks, and mindset needed to build trustworthy AI products that improve over time.

Measure what matters. Improve with confidence. Build AI systems users can trust.