9 RAG Architectures Every AI Developer Should Know: A Complete Guide with Examples

RAG optimizes language model outputs by having them reference external knowledge bases before generating responses. RAG is best suited for low-stakes environments where speed is more important than absolute factual density. In a standard setup, if a user asks a user-up-up question, RAG will follow a standard “How much does it cost?” question.