Building an AI Evaluation Harness: Foundations of AI Engineering

As AI applications move from simple prototypes to production systems, ensuring their reliability becomes a critical engineering challenge. Without a systematic way to measure performance, updating a system prompt or tweaking an agent workflow can silently degrade your application's behavior. This text-based course guides you through the foundational principles of AI engineering by teaching you how to build a custom evaluation harness from scratch. You will learn how to isolate system prompts, simulate agent behavior, and establish automated testing workflows to guarantee consistent, high-quality AI outputs. What you'll learn: Understand foundational AI evaluation concepts, metrics, and terminology before writing code; Design a custom evaluation harness to systematically test LLM outputs and agent behaviors; Extract and manage system prompts to share them consistently across different application components; Apply modern testing patterns to automate the validation of prompt changes and model updates; Implement basic performance logging and error-tracking metrics for AI applications; Practice building modular, testable AI workflows using clean code structures. You will start with the core definitions of AI evaluation and prompt management, establishing a solid theoretical foundation. From there, the written lessons walk you through designing, coding, and running your own evaluation harness step-by-step. This course is designed for aspiring AI engineers, software developers, and tech enthusiasts who want to move beyond basic prompt engineering. No prior experience with AI evaluation frameworks is required, though a basic familiarity with programming concepts is recommended. Start building your evaluation framework today to ensure your AI applications perform reliably every time.