Feb 13
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#LangChain #AI #MLOps Want to master Retrieval-Augmented Generation (RAG) using LangChain with TypeScript? This complete crash course walks you step-by-step through building a production-ready RAG system using modern AI architecture. If you're building AI apps with Next.js, multi-agent systems, or advanced LLM workflows — this is the guide you’ve been waiting for. 🔥 What You’ll Learn 1. What RAG (Retrieval-Augmented Generation) really is 2. How embeddings and vector databases work 3. How to build a RAG pipeline using LangChain (TypeScript) 4. Document loaders & chunking strategies 5. Vector stores (Pinecone, Supabase, etc.) 6. Prompt engineering for retrieval accuracy 7. How to reduce hallucinations 8. Production-ready architecture patterns 🧠 Why RAG Matters RAG is the foundation behind systems like: - ChatGPT - Perplexity AI - Claude - Google Gemini If you want to build real AI products instead of simple chatbot demos, you must understand RAG deeply. 🛠 Tech Stack LangChain (TypeScript) Vector Database Node.js Embeddings 🎯 Who This Is For - AI Engineers - Full-stack Developers - Next.js Developers - Anyone building LLM applications - Developers transitioning into AI SaaS Become a Wizard Member https://www.patreon.com/14026907/join JOIN THE AI HERO COURSE ⭐🌟✨ Join here : https://forms.gle/1B1tKJ4CzgjnBXFY6 Check out NotebookLM : https://youtu.be/qci2YEqDbFk Check out N8N Clone : https://youtu.be/jNtq3oJf6qM Check out LangChain Course : https://youtu.be/nqgAJ3f7ho8 Source code for this Video Lesson Code : https://github.com/Bienfait-ijambo/RAG-fullcoure-with-langchain TimesCode 0:00 - Introduction 01:53 - What is Retrieval Augmented Generation 14:37 - Types Chunking Technics 20:38 - Build an Embeddings & Retrieving Pipeline 29:16 - Query Decomposition 41:13 - Multi-Vector Retriever 59:58 - Contextual Compression Retriever 1:13:19 - Metadata Filtering 1:22:39 - Types Of RAG Architectures 1:24:58 - Agentic RAG 1:37:17 - Adaptive RAG with Multi-Agent system 1:47:29 - Adaptive RAG with Self-Reflective #LangChain #AI #MLOps #Python #GenerativeAI #AIAgents #RAG #MachineLearning #ArtificialIntelligence