Abstract
Effective product development relies on creating a requirements document that defines the product's technical specifications, yet traditional methods are labor-intensive and depend heavily on expert input. Large language models (LLMs) offer the potential for automation but struggle with limitations in prompt engineering and contextual sensitivity. To overcome these challenges, we developed ReqGPT, a domain-specific LLM fine-tuned on Mistral-7B-Instruct-v0.2 using 107 curated requirements lists. ReqGPT employs a standardized prompt to generate high-quality documents and demonstrated superior performance over GPT-4 and Mistral in multiple criteria based on ISO 29148. Our results underscore ReqGPT's efficiency, accuracy, cost-effectiveness, and alignment with industry standards, making it an ideal choice for localized use and safeguarding data privacy in technical product development.
| Originalsprache | Englisch |
|---|---|
| Zeitschrift | Proceedings of the Design Society |
| Jahrgang | 5 |
| Seiten (von - bis) | 2741-2750 |
| Seitenumfang | 10 |
| DOIs | |
| Publikationsstatus | Erschienen - 01.08.2025 |
| Veranstaltung | 25th International Conference on Engineering Design, ICED 2025 - Dallas, USA / Vereinigte Staaten Dauer: 11.08.2025 → 14.08.2025 |
Bibliographische Notiz
Publisher Copyright:© The Author(s) 2025.
Fachgebiete und Schlagwörter
- Ingenieurwissenschaften
ASJC Scopus Sachgebiete
- Software.
- Modellierung und Simulation
- Angewandte Informatik
- Computergrafik und computergestütztes Design
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