LangChain Full Course in One Shot | Learn RAG, LangChain, and AI App Development (Complete Tutorial)

Description

Master LangChain from scratch in one power-packed session — learn RAG, LangGraph, vector stores, retrievers, and AI workflow automation step by step! Welcome to the LangChain Full Course in One Shot — the only video you need to learn LangChain, LangGraph, and Retrieval-Augmented Generation (RAG) from the ground up! 🚀 In this complete guide, you’ll learn everything about building intelligent AI workflows, connecting language models with real-world data, and developing powerful end-to-end AI applications. Whether you’re a beginner exploring LangChain or a developer looking to master RAG pipelines, this one-shot tutorial will take you from theory to implementation with real project examples and hands-on coding. 🧠 What You’ll Learn - What LangChain is and how it connects LLMs with data and tools - How to use Text Splitters, Embeddings, and Vector Stores effectively - Building Retrievers and RAG pipelines for context-aware generation - Introduction to LangGraph and graph-based AI workflows - Creating tool-calling agents and multi-step reasoning systems - End-to-end AI app development using Python and LangChain - Best practices for prompt engineering, debugging, and optimization ⚙️ Prerequisites - Basic knowledge of Python 🐍 - Understanding of LLMs (like GPT or Ollama) and basic programming concepts - Installed libraries: langchain, openai or compatible models 🎓 Key Takeaways By the end of this course, you’ll be able to: ✅ Build complete LangChain projects — from document loaders to retrieval systems ✅ Implement RAG pipelines for accurate, context-aware AI responses ✅ Design LangGraph workflows for multi-agent and tool-based reasoning ✅ Deploy your AI applications for real-world usage If this full course helped you learn LangChain faster, smash the Like 👍 button, Subscribe 🔔 for more AI development tutorials, and share it with your fellow developers! 💭 Got a question or want me to cover advanced LangGraph or RAG concepts next? 👉 Drop your thoughts in the comments — I’d love to hear what you’re building! Timestamp: ----------------------------------------------------------------------------------------- 00:00 - What is LangChain 18:04 - Create GitHub Repository 19:44 - Virtual Environment 21:32 - Requirements.txt 24:38 - Practical 1 LLM integration 29:21 - Prompt Template 37:42 - Practical 2 54:53 - Structured Output 01:09:14 - Practical 3 01:29:38 - Output Parser 01:38:33 - Practical 4 02:07:15 - Chains 02:15:25 - Practical 5 03:18:38 - Document Loader 03:19:09 - RAG Architecture 03:32:08 - Practical 6 04:12:09 - Text Splitters 04:27:10 - Practical 7 04:52:47 - Embeddings 04:59:44 - Practical 8 05:18:38 - Vector Store 05:27:02 - Practical 9 06:03:36 - Retrievers 06:12:26 - Practical 10 06:46:32 - RAG Project -------------------------------------------------------------------------------------------- #LangChain #LangGraph #RAG #RetrievalAugmentedGeneration #AI #ArtificialIntelligence #MachineLearning #GenerativeAI #PythonAI #AIWorkflows #AIAgents #PromptEngineering #LangChainFullCourse #AppDevelopment #LangChainDevelopers #AICoding