Utilizing LLMs for Industrial Process Automation: A Case Study on Modifying RAPID Programs
This addresses the challenge for enterprises in automating proprietary industrial software with minimal effort, though it is incremental as it builds on existing few-shot methods.
The paper tackled the problem of using LLMs for industrial process automation with specialized languages like RAPID, showing that few-shot prompting can solve simple tasks without domain-specific training, enabling on-premise deployment for data security.
How to best use Large Language Models (LLMs) for software engineering is covered in many publications in recent years. However, most of this work focuses on widely-used general purpose programming languages. The utility of LLMs for software within the industrial process automation domain, with highly-specialized languages that are typically only used in proprietary contexts, is still underexplored. Within this paper, we study enterprises can achieve on their own without investing large amounts of effort into the training of models specific to the domain-specific languages that are used. We show that few-shot prompting approaches are sufficient to solve simple problems in a language that is otherwise not well-supported by an LLM and that is possible on-premise, thereby ensuring the protection of sensitive company data.