Agentic RAG in LangGraph 🤖

Welcome to the LangGraph Series! 🌙 In this session, we’re building Agentic RAG in LangGraph — an advanced RAG workflow where the system can Retrieve → Grade → Rewrite → Generate to produce better, more accurate answers. 🤖⚙️ If you’ve used basic RAG before but want to make it smarter with document grading, query rewriting, and conditional decision-making, this video will make the full workflow clear. By the end of this lesson, you’ll understand: ✅ What Agentic RAG is and why it’s better than basic RAG ✅ How to build a Retrieve → Grade → Rewrite → Generate workflow ✅ How LangGraph controls the RAG pipeline using graph logic ✅ How to grade retrieved documents for relevance ✅ How to rewrite queries when retrieval quality is poor ✅ How to generate final answers using relevant context ✅ Real‑world use cases like chatbots, knowledge assistants, and enterprise search With hands‑on examples and clear explanations, you’ll gain the confidence to build smarter, more reliable, and production‑ready RAG systems using LangGraph. 🚀 💬 Don’t forget to Like, Subscribe, and Comment what you learned today — your support keeps these coding lessons going strong! ✨ Before you sleep, make sure you’ve learned something new. ✨ #LangGraph #AgenticRAG #RAG #AIAgents #LLM #LangChain #ArtificialIntelligence #Python #AIEngineering #VectorDatabase #AgenticAI #CodeBeforeYouSleep #YashJain