Evaluating LLMs and Generating Synthetic Data with Azure AI

Building reliable generative AI applications requires rigorous testing, but gathering real-world validation data can be slow and expensive. This text-based course guides you through assessing language model performance and creating high-quality synthetic datasets. You will learn how to set up systematic evaluation workflows, use built-in and custom metrics, and generate realistic test data to benchmark your AI applications with confidence. In this course, you will: 1. Understand core evaluation concepts and LLM performance metrics. 2. Configure the Azure AI Evaluation SDK to run systematic model assessments. 3. Generate high-quality synthetic datasets to bootstrap your testing pipeline. 4. Apply custom evaluation metrics tailored to your specific domain needs. 5. Analyze evaluation results to identify and mitigate model hallucinations. 6. Implement modern prompt engineering and validation workflows to ensure data quality. You will start with foundational AI evaluation concepts before progressing to hands-on SDK configurations. The material guides you through generating synthetic data, running automated benchmarks, and interpreting quality metrics through detailed text explanations and code examples. This course is designed for beginner AI developers, data analysts, and software engineers looking to implement structured testing for generative AI models. No prior experience with AI evaluation frameworks is required, though a basic understanding of Python is helpful. Start reading today to build more reliable, robust, and well-tested AI applications.