Structured Document Generation for Industrial Equipment

Abstract

We describe an application that uses large language models to generate structured documents related to industrial equipment, specifically focusing on Failure Modes and Effects Analysis (FMEAs). Our novel application uses techniques in structured document generation, in-context learning, and ensembling to create high-quality structured content that subject matter experts supervise through a user-centric interface that presents FMEA entities as UI elements. Novel evaluation metrics for structured document generation are also proposed. Our empirical results, based on 71 asset evaluations, demonstrate the individual and combined contributions of these techniques, with an overall effectiveness that varies between a recall of 0.669 and a precision of 0.91. Qualitative feedback from target users validates the practicality of the described approach to seamlessly integrate expert supervision with generative AI in a labour-saving workflow.