Complete RAG Tutorial 2026 (Free Labs)

🔗 RAG Bootcamp: https://kode.wiki/4j9Bfyw

Learn Retrieval Augmented Generation (RAG) from scratch in this comprehensive crash course! Discover how to extend Large Language Models beyond their training data using vector databases, embedding models, and advanced retrieval techniques.

What you'll learn:
✅ RAG fundamentals and common misconceptions
✅ Vector embeddings and semantic search explained simply
✅ Building your first RAG system with real code examples
✅ Chunking strategies that actually work
✅ How to evaluate and improve your RAG pipeline
✅ Cutting-edge techniques: Agentic RAG, Multi-modal RAG, and more

Whether you're building chatbots, AI assistants, or enterprise AI solutions, this crash course gives you everything you need to implement RAG successfully.

📚 Timestamps:
00:00 - Introduction to RAG Bootcamp
01:51 - What is RAG?
03:27 - Common Misconceptions about RAG
09:10 - Vector Embedding
12:45 - When RAG Isn't Useful
15:20 - Chunking Strategies
15:50 - Fixed-Sized Chunking
17:30 - Semantic Chunking
19:15 - Overlapping Chunking
20:40 - Agentic Chunking
22:10 - Lab: Document Chunking
25:30 - Why RAG is Needed (Real Use Cases)
27:45 - Evaluating RAG
29:20 - RAG Evaluation Metrics (Recall@K, Precision@K, MRR, NDCG)
32:00 - Lab: RAG Evaluation
35:15 - Future of RAG
36:00 - Cache-Augmented Generation (CAG)
38:20 - Agentic RAG
40:45 - Multi-Query RAG
43:10 - Hierarchical RAG
45:30 - Multimodal RAG
47:50 - Conclusion u0026 Key Takeaways


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