RAG Explained: Unlock the Power of AI with Hands-On Labs 🚀

🧪Try RAG Hands-On Labs for Free - https://kode.wiki/4oANO8s

Learn how RAG (Retrieval Augmented Generation) solves the challenge of connecting AI assistants like ChatGPT to massive document repositories!

In this comprehensive video, we'll show you exactly how to make AI search, read, and understand large company documents using vector embeddings and semantic search.

Ready to build your own RAG system? Access our FREE interactive labs where you can experiment with real RAG implementations, test different chunking strategies, and see vector databases in action!

🧪Try RAG Hands-On Labs for Free - https://kode.wiki/4oANO8s

📚 What You'll Learn:
• What is RAG and why it's revolutionary for AI document search
• How vector embeddings transform documents into searchable data
• The 3-step RAG process: Retrieval, Augmented, Generation
• Semantic search vs traditional search methods
• Critical chunking strategies for different document types
• RAG Demo

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⏱️ Timestamps:
00:00 - Introduction to RAG
00:24 - Why Traditional Search Methods Don't Work
00:55 - The RAG Method Explained
01:54 - Step 1: Retrieval Process
02:25 - Step 2: Augmentation Explained
03:15 - Step 3: Generation Process
03:54 - Strategies for RAG Calibration
05:01 - Practical Lab Demo Introduction
05:27 - Demo - Set up Development Environment
06:10 - Demo - Initialize Vector Database
06:29 - Demo - Chunking Strategy and Embedding
07:19 - Demo - Feed AI Brain
07:50 - Demo - Semantic Search
08:16 - Demo - Launch a Simple Web Interface
09:43 - Conclusion & Free Lab Access

🚨Check out our learning paths at KodeKloud to get started: https://kode.wiki/41NLyks

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