Development of Fine-Tuned Retrieval Augmented Language Model specialized to manual books on machine tools⁎

Abstract

This paper aims to reduce potential human errors and loads that arise from the lack of novice human operators and different interfaces in advanced machine tool industries. A digital assistant using generative artificial intelligence is designed to answer questions about machine terminologies, and operation sequences happening in an alarm state. Combining Fine-Tuning, Retrieval Augmented Generation, and Prompt Engineering, it solves problems of common-purpose large language models. The proposed system is implemented on a local server and connected to a mobile device. It shows increasing quantitative accuracy from 51% to 79% and 84% in the fine-tuned and retriever model, and the qualitative score increases from 21 to 25 in the retriever model.