Advancing Risk and Quality Assurance: A RAG Chatbot for Improved Regulatory Compliance
This addresses operational bottlenecks for employees in regulated industries, though it appears incremental as it builds on existing RAG methods.
The paper tackled the problem of processing regulatory compliance queries in risk and quality assurance by developing a RAG chatbot, which showed substantial improvements over traditional RAG approaches when evaluated on 124 real-world queries.
Risk and Quality (R&Q) assurance in highly regulated industries requires constant navigation of complex regulatory frameworks, with employees handling numerous daily queries demanding accurate policy interpretation. Traditional methods relying on specialized experts create operational bottlenecks and limit scalability. We present a novel Retrieval Augmented Generation (RAG) system leveraging Large Language Models (LLMs), hybrid search and relevance boosting to enhance R&Q query processing. Evaluated on 124 expert-annotated real-world queries, our actively deployed system demonstrates substantial improvements over traditional RAG approaches. Additionally, we perform an extensive hyperparameter analysis to compare and evaluate multiple configuration setups, delivering valuable insights to practitioners.