Designing Generative AI Architectures with LLMs and RAG

As organizations rush to adopt artificial intelligence, the ability to design robust, scalable Generative AI architectures is becoming an essential skill for modern developers and system architects. Understanding how to connect language models securely with enterprise data is the key to building truly intelligent applications. This text-based course guides you through the foundational concepts of Generative AI system design. You will transition from understanding how Large Language Models (LLMs) process text to designing complete, production-ready architectures that leverage retrieval systems, semantic search, and structured outputs. What you'll learn: - Understand the foundational mechanics of Large and Small Language Models (LLMs/SLMs) and how they process information. - Design effective prompt engineering pipelines using advanced techniques like chain-of-thought and role-based prompting. - Configure Retrieval-Augmented Generation (RAG) architectures to connect LLMs with external enterprise data sources. - Apply vector databases and semantic search strategies to store, index, and retrieve high-dimensional data efficiently. - Evaluate when to use prompting, RAG, or fine-tuning to solve specific business and technical challenges. - Explore modern AI application patterns, including agentic workflows and semantic caching for cost-effective performance. You will begin by exploring the core terminology and mechanics of language models before moving step-by-step through prompt design, vector database integration, and RAG pattern implementation. Each concept is explained through clear architectural breakdowns and practical written scenarios. This course is designed for software developers, system architects, and technology professionals who want to learn AI system design from scratch, with no prior artificial intelligence or machine learning experience required. Start reading today to master the architectural patterns shaping the future of software development.