Generative AI Application Development with RAG and LangChain

Large language models are incredibly powerful, but they lack access to your specific, real-world data. To bridge this gap, modern developers use Retrieval-Augmented Generation (RAG) to build context-aware AI applications. In this text-based course, you will learn how to design, build, and deploy custom RAG applications from the ground up. You will gain a deep understanding of how LangChain coordinates data ingestion, vector storage, and prompt orchestration to generate highly accurate, data-driven AI responses. What you'll learn: - Understand the foundational architecture of Retrieval-Augmented Generation (RAG) and how it enhances large language models. - Load and process diverse document formats using modern LangChain document loaders and text-splitting strategies. - Generate vector embeddings and store them in vector databases for efficient semantic search. - Construct robust prompts and manage conversation context using modern LangChain orchestration patterns. - Build complete generative AI applications that answer questions accurately using your own custom datasets. The course starts with fundamental AI and RAG concepts before guiding you step-by-step through document ingestion, vector database integration, and final application construction. You will read clear explanations and review practical code snippets designed to build your skills progressively. This course is designed for beginners, developers, and tech enthusiasts looking to build their first data-connected AI application. No prior background in machine learning is required, though a basic familiarity with Python is helpful. Start reading today to unlock the potential of context-aware generative AI.