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

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