RAG Explained: Enhancing LLMs with Retrieval-Augmented Generation | Balaji Hambeere posted on the topic | LinkedIn

RAG explained !! RAG (Retrieval-Augmented Generation) controls how AI systems find and use information before generating answers. It combines information retrieval with text generation so models don’t rely only on training data. It includes document ingestion, chunking, embeddings, vector databases, similarity search, and context injection. Good RAG design improves accuracy, relevance, trust, and scalability, and significantly reduces hallucinations. With RAG, you control what data the AI can ac

What is RAG (Retrieval Augmented Generation)? | IBM

RAG Architecture Explained: Practical Example in 5 Minutes!