Evaluating Generative AI Performance in Azure AI Studio

Building a generative AI application is only the first step; ensuring it delivers accurate, safe, and high-quality responses is where the real challenge lies. Without structured evaluation, it is impossible to know if your copilot is truly meeting user needs or generating incorrect answers. This written course guides you through the process of assessing and optimizing generative AI performance. You will transition from guessing how well your model performs to using data-driven evaluation metrics to systematically measure and improve response quality. What you'll learn: Understand foundational evaluation concepts and why systematic testing is critical for generative AI; Configure evaluation runs in Azure AI Studio using test datasets; Apply key performance metrics including groundedness, relevance, coherence, and fluency; Assess safety, bias, and content moderation compliance within your AI applications; Evaluate retrieval-augmented generation (RAG) systems to ensure accurate data grounding; Analyze evaluation results to iteratively refine prompt engineering and model parameters. The course begins with core terminology and evaluation theory before walking you through the practical setup of evaluation pipelines in Azure AI Studio. You will learn to interpret metrics, troubleshoot low-performing prompts, and establish a continuous improvement workflow. This course is designed for beginning AI developers, product managers, and technology enthusiasts who want to measure AI performance, with no prior experience in cloud evaluation tools required. Start reading today to build reliable, high-performing generative AI applications that your users can trust.