Using the Retrieval-Augmented Generation to Improve the Question-Answering System in Human Health Risk Assessment: The Development and Application

• DOI: 10.3390/electronics14020386
• Publication Date: 2025-01-20T14:11:13Z
• Authors: Wenjun Meng; Yuzhe Li; Lili Chen; Zhaomin Dong

While large language models (LLMs) are vital for retrieving relevant information from extensive knowledge bases, they always face challenges, including high costs and issues of credibility. Here, we developed a question answering system focused on human health risk using Retrieval-Augmented Generation (RAG). We first proposed framework to generate question–answer pairs, resulting in 300 high-quality pairs across six subfields. Subsequently, created both Naive RAG an Advanced RAG-based Question-Answering (Q&A) system. Performance evaluation the individual research subfields demonstrated that outperformed traditional LLMs (including ChatGPT ChatGLM) RAG. Finally, integrated module single subfield launch multi-knowledge base Our study represents novel application technology optimize retrieval methods assessment.