Evaluation Harness Fundamentals for AI Engineering

As AI applications move from prototypes to production, ensuring their outputs are reliable, accurate, and safe is one of the biggest challenges in modern software development. Learning how to build and use an evaluation harness is the key to systematically testing and improving your AI systems. This text-based course guides you through the fundamental principles of AI evaluation engineering, helping you transition from manual, ad-hoc testing to designing structured evaluation pipelines that measure model performance against real-world benchmarks. What you'll learn: - Understand the foundational terminology and core concepts of AI evaluation harnesses. - Define and implement structured type systems for test cases, evaluations, and scored results. - Analyze and leverage golden datasets to establish reliable baselines for model performance. - Apply modern evaluation techniques, including basic output verification and automated scoring metrics. - Integrate systematic testing methodologies to continuously monitor and improve AI application quality. We begin with key terminology and the basic concepts of evaluation engineering before moving into data modeling, scoring metrics, and dataset management. Through clear written explanations and practical code snippets, you will learn how to design robust evaluation workflows that you can apply to your own projects. This course is designed for beginner AI engineers, software developers, and data practitioners looking to implement structured testing for AI models, with no prior evaluation experience required. Start building reliable, production-ready AI systems with confidence today.