Technical Customer Service Support with RAG Fine Tuned LLaMA 3

Abstract

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.