LLM Intelligent Customer Service in Property Management Using a RAG Approach

Traditional property management services often struggle with challenges such as long response times and difficulties in effectively addressing complex tenant issues. While Large Language Models (LLMs), like ChatGPT, offer promising solutions, they are often limited by their lack of domain-specific knowledge, leading to inaccurate or irrelevant responses. To address these shortcomings, this paper introduces an intelligent customer service system tailored for property management. The system integrates a customized LLM with the Retrieval Augmented Generation (RAG) framework, designed to provide accurate, context-aware, and personalized responses. By employing an intelligent agent to pull relevant data from a dedicated property management knowledge base, the system can engage tenants in real-time through an interactive interface, ensuring both efficiency and relevance in communication. Additionally, machine learning algorithms are employed to continuously improve the system’s performance. The architecture combines a fine-tuned LLM, a vector database for fast information retrieval, and a feedback loop that supports ongoing optimization. We anticipate that this approach will lead to greater tenant satisfaction, more personalized services, and enhanced operational efficiency. This study contributes to the broader fields of Artificial Intelligence, Robotics, and Communication by showcasing how RAG can be applied to improve customer service, with future work focusing on real-world implementation and comprehensive system evaluation.