RAG Full Course | Document Loaders to Conversational & Multi-Document RAG (LangChain)

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.