RAG Application Development with LangChain

Generative AI is transforming how we interact with information, but large language models need access to external data to provide accurate, context-aware answers. Retrieval-Augmented Generation (RAG) is the industry-standard architecture to solve this limitation. In this text-only course, you will learn how to design, build, and optimize RAG applications using the LangChain framework, bridging the gap between raw documents and intelligent conversational agents. What you'll learn: - Understand the core concepts of RAG, vector embeddings, and semantic search - Load, chunk, and process diverse document sources using LangChain document loaders - Configure and query vector databases to retrieve relevant context efficiently - Design effective prompt templates that guide language models using retrieved data - Build end-to-end conversational retrieval chains with memory and state management - Apply modern evaluation techniques to measure and improve your RAG system's accuracy You will start with the fundamental terminology of generative AI and document processing before advancing to practical text-based implementation. You will read clear explanations, analyze code snippets, and build fully functional retrieval pipelines step-by-step. This course is designed for software developers, data enthusiasts, and beginners looking to enter the world of AI engineering. No prior experience with LangChain or AI models is required, though a basic understanding of Python is helpful. Start reading today to unlock the power of context-aware generative AI applications.