Optimizing Customer Support with LLM Chatbots

Rohit Nishad, Ayan Rajput

Abstract: This paper explores the optimization of customer support systems through the integration of Large Language Model (LLM) chatbots. With the rapid advancement of artificial intelligence technologies, LLMs have demonstrated remarkable capabilities in natural language understanding and generation. We present a comprehensive framework for implementing LLM-powered chatbots in customer support environments, focusing on key performance metrics including response time, resolution rate, and customer satisfaction. Through a systematic analysis of real-world implementations across different industries, we identify optimal configuration parameters, training methodologies, and integration strategies that maximize the effectiveness of LLM chatbots in customer service applications. Our findings reveal that properly tuned LLM chatbots can reduce response times by up to 78%, increase first-contact resolution rates by 45%, and improve overall customer satisfaction scores by 32% compared to traditional customer support systems. We also discuss challenges including hallucination management, security concerns, and cost optimization strategies, providing practical guidelines for organizations seeking to enhance their customer support capabilities through LLM integration.

Keywords: Large Language Models, Chatbots, Customer Support, Artificial Intelligence, Natural Language Processing, Retrieval Augmented Generation