ENHANCING KNOWLEDGE-INTENSIVE CUSTOMER SUPPORT IN REGULATED INDUSTRIES THROUGH RETRIEVAL-AUGMENTED GENERATION (RAG): ARCHITECTURE, ALGORITHMS, AND EMPIRICAL EVALUATION

Customer support systems in regulated industries such as insurance, banking, healthcare, and telecommunications operate under strict requirements related to factual accuracy, regulatory compliance, auditability, and response latency.Customer service agents are routinely required to interpret complex contractual documents, regulatory guidelines, and procedural manuals while interacting with customers in real time.Traditional customer support technologies, including static knowledge bases and rule-based chatbots, are insufficient for these environments due to their limited contextual awareness and inability to adapt to evolving enterprise knowledge.More recently, large language models (LLMs) have demonstrated impressive conversational capabilities; however, when deployed without external grounding, they pose significant risks due to hallucinated or unverifiable responses.This paper presents a comprehensive Retrieval-Augmented Generation (RAG) framework designed specifically for knowledge-intensive customer support in regulated industries.The proposed system integrates semantic retrieval over enterprise knowledge repositories with controlled language model generation to produce responses that are contextually relevant, factually grounded, and traceable to Ramesh Chandra Aditya Komperla