Evaluating Generative AI Applications: Metrics and Best Practices

Building a generative AI application is only the first step; the real challenge lies in proving that its outputs are safe, accurate, and reliable. As large language models become central to modern software, knowing how to systematically assess their performance is an essential skill for developers and product creators. This text-only course guides you through the fundamental methodologies of evaluating generative AI systems. You will transition from relying on ad-hoc, manual checks to implementing structured, repeatable evaluation strategies that ensure your applications meet production-grade standards. What you'll learn: Understand core evaluation concepts, including qualitative versus quantitative assessment methods; Define key performance metrics for text generation, such as accuracy, coherence, and relevance; Explore modern evaluation paradigms, including the LLM-as-a-judge pattern and automated scoring; Assess Retrieval-Augmented Generation (RAG) systems using specialized retrieval and generation metrics; Identify safety risks, bias, and hallucinations in model outputs using systematic testing frameworks; Design repeatable evaluation workflows to monitor application performance over time. The course begins with foundational definitions of evaluation metrics before moving into practical assessment strategies, testing paradigms, and modern workflows for continuous monitoring. Through detailed written explanations and illustrative code snippets, you will build a solid framework for assessing AI quality. Designed for software developers, product managers, and AI enthusiasts who are new to model evaluation, this course requires no prior experience with machine learning operations. Start reading today to build trust and reliability in your generative AI applications.