Retrieval-Augmented Generation (RAG) frameworks, which merge the capabilities of retrieval systems and generative models, significantly enhance the relevance and accuracy of responses produced by large language models (LLMs). These frameworks retrieve relevant documents from a large corpus and use these documents to inform the generative process, thereby improving the contextuality and precision of the generated content.
