What Is RAG? Retrieval-Augmented Generation Explained Simply.

What is RAG, and why is it becoming such an important part of modern AI? In this video, I explain Retrieval-Augmented Generation (RAG) in simple terms and show exactly how it works. You’ll see how an AI system can search external information, retrieve the most relevant content, and use that context to generate a better-informed answer. We’ll break down: • What Retrieval-Augmented Generation actually means • How RAG works step by step • Documents, chunks, and embeddings • Semantic search and vector databases • How information is retrieved before reaching an LLM • RAG vs fine-tuning and model training • Why RAG can still produce incorrect answers • How companies use RAG with private and frequently updated information • Real-world RAG use cases By the end, concepts like embeddings, retrieval, semantic search, vector databases, and grounded AI responses should make much more sense. The simplest way to remember RAG: Question → Search → Retrieve → Generate. Subscribe to ENKAY STUDIO for practical AI tutorials, explainers, tools, and workflows. #RAG #RetrievalAugmentedGeneration #ArtificialIntelligence #GenerativeAI #LLM #AIExplained #MachineLearning #VectorDatabase #AITutorial #ENKAYSTUDIO