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Ontology-Driven LLM Assistance for Task-Oriented Systems Engineering

Topics: AI techniques for traceability, consistency, and completeness of system engineering information; Automation and AI-support of MBSE activities; LLMs-assisted Model Development; Natural language processing applied to modelling, including Large Language Models (LLM) and Generative AI; Requirements Engineering Using LLMs

Authors: Jean-Marie Gauthier ; Eric Jenn and Ramon Conejo

Affiliation: IRT Saint Exupéry, 3 Rue Tarfaya, 31400 Toulouse, France

Keyword(s): Systems Engineering, Large Language Model, Agent, Systems Modelling, Modelling Assistant, Ontology.

Abstract: This paper presents an LLM-based assistant integrated within an experimental modelling platform to support Systems Engineering tasks. Leveraging an ontology-driven approach, the assistant guides engineers through Systems Engineering tasks using an iterative prompting technique that builds task-specific context from prior steps. Our approach combines prompt engineering, few-shot learning, Chain of Thought reasoning, and Retrieval-Augmented Generation to generate accurate and relevant outputs without fine-tuning. A dual-chatbot system aids in task completion. The evaluation of the assistant’s effectiveness in the development of a robotic system demonstrates its potential to enhance Systems Engineering process efficiency and support decision-making.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Gauthier, J.-M., Jenn, E. and Conejo, R. (2025). Ontology-Driven LLM Assistance for Task-Oriented Systems Engineering. In Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering - MBSE-AI Integration; ISBN 978-989-758-729-0; ISSN 2184-4348, SciTePress, pages 383-394. DOI: 10.5220/0013441100003896

@conference{mbse-ai integration25,
author={Jean{-}Marie Gauthier and Eric Jenn and Ramon Conejo},
title={Ontology-Driven LLM Assistance for Task-Oriented Systems Engineering},
booktitle={Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering - MBSE-AI Integration},
year={2025},
pages={383-394},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013441100003896},
isbn={978-989-758-729-0},
issn={2184-4348},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering - MBSE-AI Integration
TI - Ontology-Driven LLM Assistance for Task-Oriented Systems Engineering
SN - 978-989-758-729-0
IS - 2184-4348
AU - Gauthier, J.
AU - Jenn, E.
AU - Conejo, R.
PY - 2025
SP - 383
EP - 394
DO - 10.5220/0013441100003896
PB - SciTePress

- Science and Technology Publications, Lda.
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