From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical
  Regulatory Compliance Process
Jaewoong Kim Moohong Min
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.