Testing and Troubleshooting AWS GenAI and RAG Systems

Building generative AI applications is only the first step; ensuring they are reliable, accurate, and production-ready is the real challenge. This text-based course guides you through the essential methodologies needed to test, validate, and debug intelligent applications built on AWS. You will transition from guessing why an AI model failed to systematically identifying and resolving issues in your retrieval and generation pipelines. What you'll learn: - Understand foundational concepts of generative AI evaluation, RAG architectures, and AWS-specific services. - Validate retrieval accuracy by analyzing document chunking, embeddings, and vector database query performance. - Implement systematic testing methodologies to measure LLM response relevance, truthfulness, and alignment. - Diagnose and troubleshoot common system bottlenecks, latency issues, and hallucination patterns. - Configure modern observability workflows to monitor application behavior and track quality metrics over time. This course begins with core terminology and testing fundamentals before moving into step-by-step diagnostic workflows and optimization techniques. It is designed for software developers, cloud engineers, and technical beginners who want to build dependable AI systems, requiring no prior background in machine learning evaluation. Start mastering AWS GenAI validation and build more dependable applications today.