Proposal of Retrieval-Augmented Generation Method Using Structured Documents Created by LLMs

Abstract

In recent years, retrieval-augmented generation (RAG) using large language models (LLMs) has attracted significant attention. However, many real-world documents contain structured content such as tables and images, which present challenges for conventional RAG systems. This study proposes a method for generating unified structured documents by combining OCR, layout analysis, and LLMs to preserve structural information. Experimental evaluations using university AI guidelines demonstrate that our approach improves the accuracy of RAG-generated outputs.