Foundations of Cloud AI & RAG

This unit introduces the historical, conceptual, and practical foundations of artificial intelligence and generative systems with a cloud-first focus on AWS managed services. Students will learn core AI/ML terminology, trends, model selection, retrieval-augmented generation (RAG), prompt engineering, MLOps phases, and responsible AI principles to prepare for hands-on architecture and implementation in subsequent units.

Learning Objectives

• Analyze the historical evolution and key trends in AI and ML to articulate how cloud-based services have shaped modern AI applications and user interactions.
• Demonstrate understanding of basic AI and generative AI concepts, terminology, foundation model types, and AWS-managed AI/ML offerings by accurately explaining their roles and trade-offs.
• Apply model selection, prompt engineering, and retrieval-augmented generation (RAG) workflows to design a high-level AWS-based generative AI solution that maps components to appropriate services.
• Evaluate MLOps phases and responsible AI principles—including bias mitigation, data governance, and operational risk—and propose mitigation strategies for a cloud-deployed AI system.