Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is an architecture pattern that enhances a large language model’s responses by first retrieving relevant information from external knowledge sources—such as documents, databases, or APIs—and then feeding that retrieved context into the model alongside the user’s query. Rather than relying solely on what the model memorized during pre-training, RAG lets it look…