The Ultimate RAG Guide You Need - LangChain TypeScript | RAG Tutorials
#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
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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