AWS Generative AI Model Evaluation and Troubleshooting

Deploying generative AI models is only the first step; ensuring they remain accurate, safe, and aligned with user expectations is where the real work begins. This text-based course guides you through the fundamental principles of evaluating and troubleshooting AI models within the AWS ecosystem. You will transition from basic deployment to establishing robust quality control for your AI systems. You will learn how to measure model performance across multiple dimensions, set up automated testing, and implement continuous feedback loops to detect and resolve issues before they affect your users. What you will learn: 1. Understand foundational terminology and the core metrics used to evaluate generative AI outputs. 2. Configure multi-dimensional evaluation strategies to assess model safety, accuracy, and bias. 3. Implement automated testing workflows on AWS to systematically validate model performance. 4. Establish feedback loops to capture real-world user interactions and monitor model drift. 5. Apply modern evaluation patterns such as LLM-as-a-judge and retrieval-augmented generation (RAG) triaging. 6. Troubleshoot common deployment issues, latency bottlenecks, and hallucination patterns. The course begins with essential concepts of model assessment before moving into practical AWS testing workflows, feedback architecture, and systematic debugging strategies. This course is designed for beginner cloud practitioners, developers, and data enthusiasts looking to specialize in generative AI operations. No prior model evaluation experience is required. Start reading today to build reliable, high-performing generative AI systems on AWS.