Testing, Validating, and Troubleshooting AWS Generative AI

Building a Generative AI application is only half the battle; ensuring it produces accurate, safe, and reliable results is where the real challenge begins. This text-based course guides you through the essential methodologies for evaluating and debugging modern AI systems built on AWS. You will transition from guessing if your LLM outputs are correct to using structured validation frameworks, learning how to identify hallucinations, measure retrieval quality in RAG systems, and optimize your application's performance. What you'll learn: - Understand the core metrics and methodologies for evaluating LLM outputs and RAG architectures. - Configure Amazon Bedrock and SageMaker for systematic model testing and validation. - Identify, debug, and mitigate common GenAI issues like hallucinations and format drift. - Implement AWS guardrails and safety filters to ensure compliant and secure model responses. - Monitor live model performance and set up basic observability workflows for AI applications. Starting with fundamental concepts of LLM evaluation and testing terminology, you will progress through structured text-based lessons detailing how to validate retrieval-augmented generation systems and troubleshoot performance bottlenecks on AWS. This course is designed for beginner developers, cloud enthusiasts, and QA professionals looking to specialize in Generative AI quality assurance. No advanced machine learning background is required. Start reading today to build reliable, production-ready AI solutions on AWS.