From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical Regulatory Compliance Process
This addresses the problem of high human resource demands in pharmaceutical regulatory compliance, though it appears incremental as an enhancement to existing RAG methods.
The study tackled the challenge of navigating complex pharmaceutical regulatory guidelines by introducing a QA-RAG chatbot model that integrates generative AI with RAG to retrieve and answer based on guidelines, resulting in significant accuracy improvements over baselines.
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.