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In this complete Retrieval Augmented Generation (RAG) masterclass, we build a RAG system from scratch and gradually upgrade it into more powerful, real-world pipelines. This is a deep, end-to-end video (≈4 hours) designed to give you a clear conceptual understanding of how RAG works internally — and how to implement it confidently on your own. 🔍 What you’ll learn in this video: Document Loaders and how data enters a RAG system Text Splitting strategies and why chunking matters Embeddings: how text is converted into vectors Vector Stores and similarity search Retrievers and retrieval strategies Building a Basic RAG pipeline step by step Explainable RAG with source documents Conversational RAG using memory Handling conversation history and context Multi-Document Retrieval RAG for scalable systems By the end of this video, you’ll have a strong mental model of RAG, understand why each component exists, and be able to design and extend your own RAG pipeline for real applications. This video is ideal for: AI & Machine Learning engineers LangChain learners Developers building LLM applications Anyone preparing for real-world RAG systems 📌 Subscribe to the channel for upcoming videos where we go deeper into optimization, agents, and production-grade RAG systems.