Generative AI System Design: Architectures and Applications

Building generative AI applications requires more than just calling an API; it demands a robust system architecture that is scalable, cost-effective, and secure. This text-based course guides you through the core principles of Generative AI system design, helping you transition from understanding basic model interactions to designing complete, production-ready architectures. Through clear written explanations and architectural breakdowns, you will learn how to integrate text, image, speech, and video technologies into cohesive software systems. What you'll learn: - Understand foundational generative AI concepts, model modalities, and system design terminology. - Design robust architectures for text generation, image synthesis, speech processing, and video applications. - Apply Retrieval-Augmented Generation (RAG) patterns and vector databases to ground model outputs in real-world data. - Evaluate system performance, latency, and costs across different model hosting and API strategies. - Implement safety guards, moderation pipelines, and observability tools for production reliability. - Analyze real-world case studies of multi-modal AI systems to solve complex business problems. We begin with essential terminology and foundational AI concepts before exploring specific architectural patterns for various modalities. Through structured written lessons and conceptual diagrams represented in text, you will study how to orchestrate models, databases, and APIs into cohesive systems. This course is designed for aspiring AI engineers, software developers, and system architects who are new to generative AI and want to build a strong foundational understanding of system design. No prior machine learning experience is required. Start reading today to master the architectural blueprints of modern generative AI.