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.
翻译：近年来，许多出版物探讨了如何最佳地利用大型语言模型（LLMs）进行软件工程。然而，这些研究大多集中于广泛使用的通用编程语言。在工业过程自动化领域，软件通常采用高度专业化且仅在专有环境中使用的语言，LLMs在此类软件中的应用潜力仍未得到充分探索。本文研究了企业在无需投入大量精力训练针对特定领域语言模型的情况下，能够独立实现的目标。我们证明，通过少量示例提示方法足以解决LLM原本支持不佳的语言中的简单问题，并且可在本地部署实现，从而确保敏感公司数据的安全。
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