RAG Explained in 10 Minutes

What RAG (Retrieval-Augmented Generation) is and why everyone in AI is talking about it? If you still think bigger context windows will replace Retrieval-Augmented Generation, this video will change your mind. In this video, we will understand: ✅ What is RAG? ✅ RAG Architecture explained step-by-step ✅ Chunking strategies ✅ Embedding models ✅ Vector Databases ✅ Hybrid RAG ✅ Agentic RAG ✅ Graph RAG ✅ Advanced RAG Patterns ⏱️ Timestamps: 00:00 – Introduction to RAG 01:03 – What is RAG? 03:40 – Top 3 RAG Myths Debunked 05:20 – RAG Architecture, Chunking Strategies, Embedding Models & Vector Databases 09:00 – Next video You’ll learn: What RAG is and why it beats prompting alone for many production use cases The biggest myths around RAG, including why “long context kills RAG” is wrong How RAG architecture works end-to-end: chunking, embeddings, retrieval, reranking, and generation How to think about vector databases and embedding model choices The 10 essential RAG patterns you should understand in 2026, including simple RAG, branched RAG, HyDE, agentic RAG, Graph RAG, and more Next Video: Complete End-to-End RAG Project with LangChain + Vector Database + FastAPI Tools and technologies mentioned: LangChain LlamaIndex Pinecone Weaviate Qdrant Milvus Chroma OpenAI text-embedding-3-large Voyage 3 BGE-large E5-Mistral #RAG #LLM #AIEngineering #GenAI #VectorDatabase ❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.