RAG Explained: Retrieval Augmented Generation | AI Explained #12

RAG Explained: Retrieval Augmented Generation – episode 12 of AI Concepts Explained, a free beginner-friendly AI and machine learning course by Irfan Hayat. Retrieval augmented generation (RAG) lets AI answer from your own documents. Irfan Hayat explains chunking, embeddings, vector search, prompt augmentation and evaluation with animated diagrams and a mini RAG retriever in Python. What you'll learn • Why LLMs need outside knowledge • The RAG pipeline step by step • Chunking documents and building a vector index • A mini retriever in Python • RAG vs fine-tuning, and common pitfalls Chapters 00:00 Intro 00:38 Why LLMs need outside knowledge 01:11 The RAG pipeline 01:48 A mini retriever in Python 02:27 Making RAG work well 03:03 RAG vs fine-tuning 03:37 Chunking in Python 04:30 Vector databases 05:22 Evaluating a RAG system 06:13 A RAG request, end to end 07:03 Quick quiz 07:48 Recap 08:15 Next: AI agents All slides, code examples and explanations are original. Narration uses a synthetic (text-to-speech) voice reading the instructor's original script. No music, third-party footage or logos are used. ▶ Full series: AI Concepts Explained playlist #RAG #ArtificialIntelligence #LLM