Generative AI System Design Foundations

Building a generative AI prototype is easy, but scaling it into a reliable, production-ready system requires a solid understanding of infrastructure and architecture. This text-based course guides you through the fundamental principles of designing robust generative AI systems from the ground up. You will progress from understanding basic machine learning deployments to architecting complex, distributed AI applications. By exploring real-world patterns, you will learn how to balance performance, latency, and cost while ensuring your AI systems remain scalable and secure. What you'll learn: 1. Understand the core components of generative AI architecture, including foundation models, APIs, and orchestration layers. 2. Design retrieval-augmented generation (RAG) patterns to connect LLMs with external vector databases. 3. Apply distributed system principles to manage model serving, caching, and load balancing. 4. Implement basic MLOps workflows for model monitoring, evaluation, and rate limiting. 5. Optimize system latency and operational costs through efficient prompt engineering and token management. The course begins with essential terminology and structural foundations before moving into practical architectural patterns and step-by-step system design walkthroughs. You will analyze written case studies and work through detailed system design scenarios. This course is designed for software engineers, aspiring system architects, and tech enthusiasts who want to transition into AI engineering. No prior experience with system design or machine learning production is required. Start reading today to master the architectural patterns shaping the future of artificial intelligence.