Retrieval-Augmented Generation (RAG) is rapidly transforming the way AI systems handle knowledge and generate responses. Unlike traditional generative AI models that rely solely on training data, RAG architectures combine retrieval mechanisms with generation models, enabling more accurate, context-aware, and up-to-date responses. This guide explores 9 RAG architectures every AI developer should understand, complete with practical examples and insights on implementation.