Використання RAG для покращення відповідей у великих мовних моделях

Abstract

This paper examines the Retrieval-Augmented Generation (RAG) approach as an effective method for improving the accuracy, relevance, and reliability of large language model responses. It describes the principle of integrating external knowledge sources into the response generation process, which allows the model to process relevant information and reduce the risk of hallucinations. The key components of the RAG architecture, indexing and search mechanisms, and advantages over traditional LLMs that operate solely on the basis of prior training are analyzed.