RAG Crash Course for Beginners

🧪RAG Labs for Free: https://kode.wiki/3KfeX1a Ever wondered how ChatGPT remembers your documents or how AI searches through company data? The secret is RAG (Retrieval Augmented Generation)! In this hands-on RAG tutorial, we will show you exactly how to build production-ready RAG systems from scratch. No fluff, just practical coding examples you can follow along with. What makes this video different? You get a real lab environment to practice everything we cover! 🧪RAG Labs for Free: https://kode.wiki/3KfeX1a ⚡ Quick Overview: • RAG Components Overview • Vector Search & Embedding Models • ChromaDB and VectorDB • Document Chunking Strategies • Complete RAG Pipeline Build 🚨Start Your AI Journey with KodeKloud: https://kode.wiki/41NLyks ⏰ TIMESTAMPS: 00:00 - Introduction to RAG Tutorial 01:15 - Simplest RAG Explanation 03:32 - When not to RAG? 07:40 - What is RAG? 11:49 - Free Lab 1: Keyword Search (TF-IDF & BM25) 15:02 - What are Semantic Search? 16:54 - Understanding Embedding Models 19:00 - Embeddings and Vectors 21:00 - The Dot Product 26:00 - Lab 2: Embedding Models 29:50 - Vector Databases Explained 33:04 - ChromaDB Tutorial 34:45 - Lab 3: Vector Databases 38:17 - Chunking Explained 39:39 - Document Chunking Strategies 43:22 - Lab 4: Document Chunking 48:45 - Build your RAG Architecture 49:31 - Lab 5: Complete RAG Pipeline 51:50 - Caching, Monitoring and Error Handling 56:34 - RAG in Production 58:08 - Conclusion #RAG #RetrievalAugmentedGeneration #Vectordb #AI #EmbeddingModels #VectorDatabase #ChromaDB #AITutorial #SemanticSearch #LLM #OpenAI #DocumentChunking