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


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