From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical Regulatory Compliance Process

Regulatory compliance in the pharmaceutical industry involves navigating complex and voluminous guidelines, often requiring significant amounts of human resources. Recent advancements in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) methods provide promising enhancements to data processing and knowledge management, potentially easing these burdens. However, despite these advancements, conventional Retrieval-Augmented Generation (RAG) methods fall short in this domain due to inherent structural problems. To address these challenges, we introduce the Question and Answer Retrieval Augmented Generation (QA-RAG) framework. This framework enhances the conventional RAG framework. It integrates a dual-track retrieval mechanism tailored to the specific and dynamic nature of pharmaceutical regulations. It utilizes not only the original query but also the answers generated by a fine-tuned LLM, thus providing a more robust foundation for document retrieval. Our experiments demonstrate that QA-RAG outperforms conventional methods in various evaluation metrics including precision, recall, and F1-score. These results underscore QA-RAG's capability to enhance both the accuracy and efficiency of regulatory compliance processes in the pharmaceutical industry. This paper details the structure and efficacy of QA-RAG, emphasizing its potential to revolutionize the regulatory compliance process in the pharmaceutical industry and beyond.