AI Engineering Fundamentals: Model Evaluation and Scoring Metrics

Building AI applications is only half the battle; knowing how to measure their performance and accuracy is what separates hobbyists from professional AI engineers. As language models become more complex, establishing rigorous evaluation pipelines is critical to ensuring reliable outputs. This text-based course guides you through the essential concepts of AI evaluation (evals). You will transition from guessing if your model works to systematically measuring its performance using industry-standard scoring methodologies. What you'll learn: Understand the fundamental terminology of AI model evaluation and why systematic testing is critical; Compare manual evaluation strategies with automated scoring methods, including LLM-as-a-judge patterns; Calculate and apply key evaluation metrics such as pass@k to measure model generation accuracy; Distinguish between capabilities evaluations and regression testing to prevent performance degradation; Design basic evaluation workflows that can be integrated into modern development pipelines. You will start by mastering foundational terminology and basic evaluation concepts before moving on to practical scoring formulas, automated workflows, and regression testing strategies. The material is presented entirely through clear written explanations, practical scenarios, and conceptual code snippets. This course is designed for aspiring AI engineers, software developers, and tech professionals who want to understand how to evaluate AI systems. No advanced background in machine learning is required. Start building reliable, measurable AI systems today.