Introduction:
A natural language processing (NLP) architecture called Retrieval-Augmented creation (RAG) combines the best aspects of retrieval-based and generative models to enhance performance on a range of NLP tasks, most notably text creation and question answering.
Given a query, a retriever module in RAG is used to quickly find pertinent sections or documents from a sizable corpus. The information included in these extracted sections is fed into generative models, like language models or transformer-based models like GPT (Generative Pre-trained Transformer). After that, the query and the information that was retrieved are processed by the generative model to produce a response or answer.
RAG's primary benefit is its capacity to combine the accuracy of retrieval-based methods for locating pertinent data with the adaptability and fluency of generative models for producing natural language responses. Compared to using each method separately, RAG seeks to generate outputs that are more accurate and contextually relevant by combining these approaches.
RAG has demonstrated its usefulness in utilizing the complementary strengths of retrieval and generation in NLP systems by exhibiting promising outcomes in a variety of NLP tasks, such as conversational agents, document summarization, and question answering.