Learn AI with KodeKloud - 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 & Key Takeaways 🎓 More AI Courses: #RAG #AI #MachineLearning #LLM #ChatGPT #ArtificialIntelligence #VectorDatabase #OpenAI #DeepLearning #DataScience #kodekloud