Technical Customer Service Support with RAG Fine Tuned LLaMA 3

Providing effective technical customer service support is acritical challenge for organizations managing complex productecosystems. This paper explores the application ofRetrieval-Augmented Generation (RAG) using a fine-tunedLLaMA 3 model to enhance customer support workflows forBogen’s E7000 system. The project involves creating a customdataset derived from Bogen’s documentation manuals totrain the model with domain-specific knowledge of the E7000system. The objective is to assist customer service representativesby developing an LLM capable of processing technicalqueries, identifying potential issues within the E7000system, and proposing solutions or troubleshooting tips. Byleveraging the RAG framework, the system dynamically retrievesrelevant context from an external knowledge base toaugment the model’s responses, ensuring scalability and precision.Results demonstrate the feasibility of deploying afine-tuned LLM to improve query processing efficiency andresponse accuracy. This work highlights the transformativepotential of advanced LLMs in delivering technical customersupport in specialized domains.