Everything about RAG (Retrieval Augmented Generation) - Simple Explanation with Examples

RAG is everywhere, like your Copilot 365, Glean, Atlassian Rovo, yet most explanations are either a one-line definition or a confusing list of "10 different types of RAG." Both miss what actually matters: how it works inside, why it fails, and what fixes each failure. I am Sathiesh Veera, I am an AI Solutions architect and I build solutions that use AI. In this video, we will talk about RAG system layer by layer, the same way it happens in a real company. We start with the basics - chunks, embeddings, and the searchable index, watch the basic setup fail in four specific ways, and fix each failure one at a time. By the end, you will know exactly what names like hybrid RAG, corrective RAG, agentic RAG, Self-RAG and GraphRAG actually refer to, and why they are NOT different types of RAG at all. Key points we discuss today: The myths: Is RAG dead? Are vector embeddings dead? Does RAG make AI "learn" your documents? The open-book exam: what RAG actually is, in one analogy The four failure classes every RAG system has: ingestion, retrieval, generation, and question type Whether you are building a RAG pipeline at work, tuning one that underperforms, or preparing for AI engineering interviews, this is the mental model that lets you diagnose any RAG system: don't ask "which type of RAG should we use" - ask "which failure are we having, and which layer fixes it." Watch the full video for the complete picture, or use the timestamps below to jump to the layer you need. Chapters: 0:00 - Introduction 0:53 - Is RAG dead? Myths we need to clear first 02:10 - What RAG really is: the open-book exam 03:36 - The 4 ways every RAG system fails 04:32 - Basic RAG: chunks, embeddings, and the index 06:57 - Failure 1 - Ingestion: the Chunking problem 09:32 - Fix: Smarter chunking: Contextual retrieval, Parent-Child, Semantic 11:26 - Failure 2 - Retrieval: why semantic search misses details 12:44 - Fix: Hybrid search and Reranking explained 15:09 - Failure 2 - Retrieval: where the Question is the Problem 16:29 - Fixing the question: Rewriting, Multi-query, HyDE 18:07 - Corrective RAG, Agentic RAG and Multi-hop RAG 19:31 – Failure 3 - Generation: Contradictions, Fabrication & Self-RAG, Claim drift 24:40 – Failure 4 - Questions RAG can never answer 26:42 - Graph RAG 27:29 – Are there really "types" of RAG? #RAG #aifundamentals #learnai #AIEngineering #TechnologyDebunk