LangGraph + Neo4j Crash Course for Beginners: AI Agent Development using Knowldege Graph | Hands-on! - Complete RAG Breakdown: (Graph, Light, Path & More!)

Ever wondered what all these different RAG methods actually mean — Naive RAG, Graph RAG, Light RAG, Path RAG, Agentic RAG, and Hybrid RAG? 🤔 In this video, I break down every major type of Retrieval-Augmented Generation (RAG) in a simple, visual, and practical way. You’ll learn: • ✅ What each RAG type actually is • ⚙️ How they work under the hood • 🧠 When and why to use each one • 💡 Real-world examples and use cases By the end, you’ll have a complete understanding of how RAG systems evolve from basic to advanced — so you can choose the right method for your own AI and LLM projects. If you’ve seen videos that are too theoretical or don’t connect the dots, this one finally puts everything together in one place. 🎥 Watch till the end for a full side-by-side comparison of all RAG types — including Naive RAG, Graph RAG, Light RAG, Path RAG, Agentic RAG, and Hybrid RAG. 00:00 Intro 00:36 Naive RAG 02:49 Graph RAG 04:14 Light RAG 6:38 Path RAG 07:55 Agentic RAG 08:52 Outro Topics Covered: RAG, Retrieval Augmented Generation, Naive RAG, Graph RAG, Light RAG, Path RAG, Agentic RAG, Hybrid RAG, RAG tutorial, RAG explained, RAG types, RAG overview, AI retrieval, LLM RAG, LangChain RAG, advanced RAG, RAG methods, RAG architectures, generative AI, knowledge retrieval, context retrieval, AI agents, agentic AI, graph-based RAG, hybrid retrieval, RAG for beginners, RAG examples, RAG guide, AI pipeline, vector database, embeddings, Pinecone RAG, production RAG, LangGraph RAG, LightRAG tutorial, document retrieval, knowledge graph, semantic search, retrieval optimization, AI development, prompt engineering, AI infrastructure, scalable RAG, memory augmented AI, retrieval pipeline, RAG deep dive, AI knowledge base, efficient RAG, modular RAG, retrieval pipeline explained, knowledge-augmented LLMs