?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?
07:56 - What is RAG?
12:18 - Free Lab 1: Keyword Search (TF-IDF & BM25)
15:33 - What are Semantic Search?
17:25 - Understanding Embedding Models
26:34 - Lab 2: Embedding Models
30:21 - Vector Databases Explained
30:15 - ChromaDB Tutorial
35:17 - Lab 3: Vector Databases
38:48- Document Chunking Strategies
43:53 - Lab 4: Document Chunking
48:16 - Build your RAG Architecture
50:02 - Lab 5: Complete RAG Pipeline
52:25 - Caching, Monitoring and Error Handling
57:07 - RAG in Production
#RAG #RetrievalAugmentedGeneration #Vectordb #AI #EmbeddingModels #VectorDatabase #ChromaDB #AITutorial #SemanticSearch #LLM #OpenAI #DocumentChunking