Enhancing Natural Language Processing: A Comprehensive Review of Retrieval Augmented Generation

Retrieval augmented generation (RAG) has emerged as a promising approach in natural language processing, combining retrieval and generation techniques to produce high-quality text. By incorporating external knowledge into the generation process, RAG addresses the limitations of generative models that rely solely on learned information. This review explores the foundational principles and architecture of RAG, which typically involves a two-step process: retrieving relevant information based on the input query and generating text informed by both the query and the retrieved knowledge. The review discusses key considerations for the retriever and generator components, as well as the integration of retrieved knowledge into the generative model. By understanding the underlying principles and architecture of RAG, researchers and practitioners can leverage this powerful technique to improve the quality and contextual relevance of text generation tasks.