AI Engineering Fundamentals: Diagram Evaluation and Output Scorers

Building AI applications is easy, but ensuring they produce reliable, high-quality results consistently is a major engineering challenge. Systematic evaluation is the key to transitioning from unpredictable prompts to production-ready AI systems. In this course, you will learn the core principles of AI engineering evaluation, focusing on diagram-based evaluation flows and output-based scorers. You will discover how to track performance, set up rigorous test cases, and refine your prompts based on structured, reproducible metrics. What you'll learn: - Understand the foundational concepts of AI evaluation and why systematic testing is essential for LLM applications. - Configure diagram-based evaluation pipelines to map and test complex multi-step AI workflows. - Implement output-based scorers to automatically grade and validate LLM responses. - Design robust test cases that target specific failure modes and edge cases in your prompts. - Apply modern evaluation patterns, including LLM-as-a-judge and structured output validation. - Analyze evaluation metrics to make data-driven improvements to your AI engineering pipeline. The course starts with essential terminology and the philosophy of AI testing before guiding you through the step-by-step implementation of custom evaluation scorers. You will read through practical examples and analyze code patterns designed to make your AI systems reliable and predictable. This course is designed for beginner AI engineers, software developers, and prompt designers who want to move beyond manual testing. No advanced machine learning background is required. Start building more reliable AI systems today by mastering the fundamentals of modern AI evaluation.