Regulatory compliance in the pharmaceutical industry entails navigating
through complex and voluminous guidelines, often requiring significant human
resources. To address these challenges, our study introduces a chatbot model
that utilizes generative AI and the Retrieval Augmented Generation (RAG)
method. This chatbot is designed to search for guideline documents relevant to
the user inquiries and provide answers based on the retrieved guidelines.
Recognizing the inherent need for high reliability in this domain, we propose
the Question and Answer Retrieval Augmented Generation (QA-RAG) model. In
comparative experiments, the QA-RAG model demonstrated a significant
improvement in accuracy, outperforming all other baselines including
conventional RAG methods. This paper details QA-RAG's structure and performance
evaluation, emphasizing its potential for the regulatory compliance domain in
the pharmaceutical industry and beyond. We have made our work publicly
available for further research and development.

该研究介绍了一种利用生成型 AI 和检索增强生成（RAG）方法的聊天机器人模型，该模型旨在搜索与用户查询相关的指南文件并基于检索到的指南提供答案。通过比较实验，QA-RAG 模型在准确性方面表现出显著提升，并超越了所有其他基线方法，包括传统的 RAG 方法。本文详细介绍了 QA-RAG 的结构和性能评估，并强调其在药品监管合规领域和其他领域的潜在应用。我们已将我们的工作公开提供以供进一步的研究和开发。