Systematic GenAI Troubleshooting on AWS

Deploying generative AI systems to production is only the first step; maintaining their reliability, accuracy, and performance under real-world conditions is where the true challenge begins. This text-based course equips you with a structured, step-by-step methodology to identify, diagnose, and resolve common failures in cloud-hosted LLM applications and retrieval-augmented generation (RAG) pipelines. What you'll learn: • Understand core failure modes of generative AI systems, including hallucinations, high latency, and drift. • Configure AWS observability and monitoring tools to track model performance and API usage. • Apply structured evaluation frameworks and metrics to measure response quality and retrieval accuracy. • Troubleshoot RAG pipelines by diagnosing vector database issues and search quality gaps. • Implement guardrails and feedback loops to continuously improve system reliability. • Practice systematic debugging workflows through detailed written scenarios and code examples. You will start with foundational troubleshooting concepts and key terminology before moving into structured monitoring, evaluation metrics, and practical debugging strategies for live AWS integrations. This course is designed for software engineers, cloud practitioners, and AI enthusiasts who want to build reliable production systems, with no advanced machine learning background required. Read this guide to master the art of maintaining robust, production-ready generative AI systems.