Evals for AI Engineers: Systematically Measuring and Improving AI Applications

Stop relying on guesswork to determine how well your AI applications perform. Evals for AI Engineers by Shreya Shankar and Hamel Husain provides a practical, systematic approach to testing, measuring, and improving AI applications, particularly those powered by Large Language Models (LLMs).

Drawing on their extensive experience working with more than 35 AI products and cutting-edge research, the authors explain how to build reliable, high-quality AI systems that perform consistently in real-world production environments.

This hands-on guide helps AI engineers, software developers, technical product managers, and technical leads establish effective evaluation processes that evolve alongside their applications.

What You Will Learn:

• Identify and Prioritize Failures: Perform systematic error analysis, identify recurring issues, and create representative test sets to uncover weaknesses in AI applications.

• Build Reliable Evaluations: Combine code-based checks, LLM-as-a-judge techniques, human feedback, and synthetic data to create trustworthy evaluation systems.

• Improve AI Application Quality: Develop continuous evaluation processes to measure and improve the accuracy, reliability, and overall performance of AI applications.

• Monitor Production Systems: Track regressions, identify performance issues, and maintain consistent quality as AI applications evolve.

• Optimize Performance and Costs: Balance application quality, response latency, and operational costs when deploying AI systems at scale.

Who Should Read This Book?

• AI Engineers and Machine Learning Engineers

• Software Engineers and AI Developers

• Generative AI and LLM Application Developers

• Technical Product Managers

• Technical Leads and Engineering Teams