Introduction to RAG: Build Reliable LLM Applications with LangChain

Large language models are powerful, but they often struggle with outdated information and factual errors. Retrieval-Augmented Generation (RAG) solves this by anchoring model responses in real, verifiable data. This written course provides a clear, step-by-step pathway to understanding and implementing this essential AI pattern. You will transition from understanding core theory to structuring data pipelines and building a functional, data-connected assistant. What you'll learn: - Understand the core architecture of Retrieval-Augmented Generation and how it prevents model hallucinations. - Configure document loaders and text splitters to prepare raw data for vector storage. - Implement vector databases and semantic search to retrieve highly relevant context. - Design effective prompts that combine retrieved knowledge with user queries. - Build end-to-end RAG pipelines using LangChain to power intelligent applications. - Create a simple user interface using Streamlit to interact with your data-connected models. The course begins with essential terminology and the conceptual mechanics of information retrieval. You will then progress through text-based guides covering data ingestion, vector embeddings, and pipeline integration. This course is designed for software developers, data enthusiasts, and beginners curious about AI engineering. No prior experience with RAG or LangChain is required, though a basic understanding of Python is helpful. Start building smarter, more reliable language model applications today.