SMART RESPONSE: RAG ENHANCED QUESTION ANSWERING MODEL

Abstract

Smart Response: RAG Enhanced Question Answering Model', aims to revolutionize question answering systems by integrating RetrievalAugmented Generation (RAG). RAG synergizes a retriever for document search with a generator like a transformer model to ensure context-rich, factual, and coherent responses. This approach helps minimize hallucinations and offers domainspecific scalability, transforming applications in customer support, education, and more. RAG improves this by fusing a generative transformer model with retrieval-based data to provide factual and contextually rich responses.This project focuses on creating a smart, enhanced question answering model by leveraging Retrieval-Augmented Generation (RAG). RAG aims to improve the accuracy and reliability of Large Language Models (LLMs) by providing them with external, dynamically updated knowledge sources, enhancing their ability to answer questions precisely and contextually. The project's abstract will likely describe the challenges in question answering, the RAG approach as a solution, the components of the RAG system (retrieval and generation), and the expected benefits of the enhanced model, such as increased accuracy, improved context understanding, and the ability to handle complex conversational settings.