Building Generative AI Applications with RAG and Vector Databases

Standard large language models often lack access to private or real-time data, limiting their usefulness for specific business needs. By combining generative AI with Retrieval-Augmented Generation (RAG), you can ground LLMs in your own custom data to deliver accurate, context-aware answers. This course guides you through the process of designing and implementing RAG architectures from scratch. You will learn how to prepare your data, store it in vector databases, retrieve relevant information, and feed it into generative models to build smarter applications. What you'll learn: - Understand the fundamental concepts of Generative AI, embeddings, and retrieval-augmented generation. - Configure and query vector databases to store and retrieve high-dimensional data efficiently. - Apply prompt engineering techniques to optimize how language models use retrieved context. - Build clean, modular RAG pipelines using modern Python conventions and framework patterns. - Implement evaluation techniques to measure the accuracy and relevance of your system's responses. Starting with foundational terminology and core AI concepts, the course transitions into practical, step-by-step written guides and code snippets. You will explore data ingestion, vector indexing, and pipeline orchestration to assemble a fully functioning RAG system. This course is designed for aspiring AI developers, software engineers, and tech enthusiasts who want to build custom AI applications; no prior experience with generative AI or vector databases is required. Start reading today to begin building your own intelligent, data-driven AI systems.