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.
| Original language | English |
|---|---|
| Journal | Proceedings of the Design Society |
| Volume | 5 |
| Pages (from-to) | 2741-2750 |
| Number of pages | 10 |
| DOIs | |
| Publication status | Published - 01.08.2025 |
| Event | 25th International Conference on Engineering Design, ICED 2025 - Dallas, United States Duration: 11.08.2025 → 14.08.2025 |
Bibliographical note
Publisher Copyright:© The Author(s) 2025.
Research areas and keywords
- fine-tuning
- large language models
- machine learning
- new product development
- requirements
- Engineering
ASJC Scopus Subject Areas
- Software
- Modelling and Simulation
- Computer Science Applications
- Computer Graphics and Computer-Aided Design
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