Utilizing LLMs for Industrial Process Automation: A Case Study on Modifying RAPID Programs

2025-11-14

AI Summary

To address the challenge of adapting large language models (LLMs) to proprietary industrial programming languages—such as ABB RAPID—in automation domains, this paper proposes a fine-tuning-free, few-shot prompting method enabling locally deployed LLMs to directly comprehend and modify RAPID programs. By eliminating reliance on large-scale annotated datasets or custom model training, the approach preserves data privacy and enhances deployment flexibility. Experimental evaluation demonstrates its effectiveness on elementary tasks including code repair and logical adaptation, substantially lowering the adoption barrier for LLMs in non-general-purpose industrial language settings. Key contributions include: (i) the first systematic investigation into LLM support for closed industrial languages like RAPID; (ii) a lightweight, secure, and plug-and-play prompting framework; and (iii) a low-overhead, highly controllable paradigm for AI-assisted programming tailored to high-sensitivity industrial environments.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

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.

Problem

• Applying LLMs to industrial automation with specialized proprietary languages
• Exploring few-shot prompting for domain-specific languages without extensive training
• Enabling on-premise solutions to protect sensitive industrial company data

Innovation

• Few-shot prompting for industrial domain-specific languages
• On-premise LLM deployment to protect sensitive data
• Modifying RAPID programs without specialized model training