Utilizing LLMs for Industrial Process Automation: A Case Study on Modifying RAPID Programs
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915618185281536 |
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| author | Fares, Salim Herbold, Steffen |
| author_facet | Fares, Salim Herbold, Steffen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11125 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Utilizing LLMs for Industrial Process Automation: A Case Study on Modifying RAPID Programs Fares, Salim Herbold, Steffen Software Engineering Artificial Intelligence 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. |
| title | Utilizing LLMs for Industrial Process Automation: A Case Study on Modifying RAPID Programs |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2511.11125 |