Designing and Building Generative AI Applications

Generative AI is transforming how we build software, but moving from simple prompts to robust, production-ready applications requires a solid understanding of system architecture. This text-based course guides you through the core concepts and design patterns needed to build reliable AI-driven solutions. You will transition from a curious developer to an architect capable of designing, implementing, and evaluating applications powered by Large Language Models (LLMs). By working through clear written explanations and structured code walk-throughs, you will understand how to integrate AI components into modern software systems safely and efficiently. What you will learn: - Understand foundational LLM architectures, key terminology, and how transformer models process text. - Apply prompt engineering techniques to programmatically guide model behavior and output structure. - Configure Retrieval-Augmented Generation (RAG) systems to connect LLMs with external data sources. - Integrate vector databases to store and query semantic embeddings efficiently. - Design secure application workflows that handle model limitations, hallucinations, and API rate limits. - Explore basic MLOps concepts to monitor, evaluate, and maintain generative AI applications in production. The journey begins with essential terminology and the core mechanics of language models, before moving step-by-step through application patterns, data integration, and production best practices. This course is designed for software developers, system architects, and tech enthusiasts who want a foundational understanding of AI application design. No prior experience with machine learning is required, though basic programming familiarity is helpful. Start reading today to master the architectural patterns of the generative AI era.