Technical Customer Service Support with RAG Fine Tuned LLaMA 3 - Jose Della Sala (Proceedings of the International Florida Artificial Intelligence Research Society Conference)
Providing effective technical customer service support is a critical challenge for organizations managing complex product ecosystems. This paper explores the application of Retrieval-Augmented Generation (RAG) using a fine-tuned LLaMA 3 model to enhance customer support workflows for Bogen’s E7000 system. The project involves creating a custom dataset derived from Bogen’s documentation manuals to train the model with domain-specific knowledge of the E7000 system. The objective is to assist customer service representatives by developing an LLM capable of processing technical queries, identifying potential issues within the E7000 system, and proposing solutions or troubleshooting tips. By leveraging the RAG framework, the system dynamically retrieves relevant context from an external knowledge base to augment the model’s responses, ensuring scalability and precision. Results demonstrate the feasibility of deploying a fine-tuned LLM to improve query processing efficiency and response accuracy. This work highlights the transformative potential of advanced LLMs in delivering technical customer support in specialized domains.