Complete RAG Tutorial 2026 - Retrieval Augmented Generation from Scratch

Learn Retrieval Augmented Generation (RAG) from scratch in this comprehensive 48-minute crash course that teaches you how to extend Large Language Models beyond their training data using vector databases, embedding models, and advanced retrieval techniques. Master RAG fundamentals while dispelling common misconceptions about the technology, then dive deep into vector embeddings and semantic search concepts explained in simple terms. Build your first RAG system with real code examples and discover chunking strategies that actually work, including fixed-sized chunking, semantic chunking, overlapping chunking, and agentic chunking through hands-on document chunking labs. Explore when RAG isn't useful and understand real-world use cases that demonstrate why RAG is essential for modern AI applications. Learn to evaluate and improve your RAG pipeline using key metrics like Recall@K, Precision@K, MRR, and NDCG through practical RAG evaluation labs. Discover cutting-edge techniques shaping the future of RAG, including Cache-Augmented Generation (CAG), Agentic RAG, Multi-Query RAG, Hierarchical RAG, and Multimodal RAG approaches. Whether you're building chatbots, AI assistants, or enterprise AI solutions, gain everything needed to implement RAG successfully in your projects with practical knowledge and hands-on experience.