Beyond RAG: A LLM-Based FAQ Matching Framework for Real-Time Decision Support in Contact Centers

Abstract

In customer contact centers, human agents often face long average handling times (AHT) due to the need to manually interpret queries and search large knowledge bases (KBs). While retrieval-augmented generation (RAG) systems using large language models (LLMs) are increasingly adopted to support these tasks, they face limitations in real-time conversations—particularly with poorly formulated queries and repeated retrieval of frequently asked questions (FAQs). To address these issues, we propose a decision support framework that extends beyond RAG by combining real-time question identification with a dual-threaded FAQ matching and generation system. If the query matches a FAQ, the answer is retrieved instantly; otherwise a well-formed query is generated and routed to a RAG model. Deployed within Minerva CQ’s human-agent assist platform, our solution delivers sub-2-second responses for matched queries, significantly reduces unnecessary RAG calls, and lowers operational costs. We also introduce an automated, LLM-agentic pipeline for mining FAQs from historical transcripts, enabling continuous improvement of the FAQ knowledge base in the absence of manually curated QA pairs.