Building RAG Applications with LangChain and NVIDIA NIM

Modern AI development requires connecting language models to real-time external data. This text-based course guides you through creating Retrieval-Augmented Generation (RAG) workflows using LangChain and NVIDIA NIM microservices to process and query live web pages. You will read through clear explanations, explore step-by-step code implementations, and gain the practical skills needed to ingest unstructured online documents and build responsive AI pipelines. What you'll learn: - Understand core concepts of Retrieval-Augmented Generation (RAG) and embedding models - Configure and query inference endpoints using NVIDIA NIM - Parse and clean raw web pages into structured text for AI consumption - Implement efficient document chunking and modern vector retrieval workflows - Build complete question-answering pipelines with LangChain - Apply prompt engineering best practices to improve response accuracy and relevance The course begins with fundamental terminology and architecture basics before progressing to hands-on Python examples and end-to-end pipeline assembly. This course is designed for beginner developers, technical analysts, and AI enthusiasts looking to build practical knowledge in LLM orchestration without complex prerequisites. Start reading today to expand your skills in modern AI engineering.