## 1 Introduction  
The development and validation of novel methodologies in the field of safety science, particularly in the areas of organisational resilience and occupational health and safety (OHS), requires rigorous testing and a substantial amount of high-quality data [1, 2]. However, obtaining this data presents a number of practical and ethical challenges, from obtaining approval for human-centred studies to navigating complex data protection regulations [3]. These barriers often limit researchers' abilities to efficiently prototype and evaluate new methods, hindering progress in areas where timely innovation is critical. To address these limitations, this study presents an integrated approach that uses generative artificial intelligence (AI) tools to create a flexible and accessible workflow workbench for testing and refining early-stage methods, specifically in the areas of occupational safety and organisational resilience. The impetus for this research stems from the development of an integrated Resilience Analysis Grid-RASCH method, a novel approach to improving organisational resilience by quantifying and analysing adaptive capacity [4, 5, 6, 7]. To assess the viability of this method without incurring the full cost and complexity of data collection and extensive real-world testing, we employ a structured workflow based on generative AI tools, such as large language models (LLMs). These tools have shown great promise in simulating complex environments and generating synthetic data, allowing researchers to model and test hypotheses in a controlled and reproducible manner before committing to extensive field trials [8; 9]. This article demonstrates how generative AI can serve as a powerful tool for prototyping and iterating new methods in an OHS context. While LLMs and other advanced neural networks typically require extensive computational resources and expertise to develop, recent advances have made accessible and relatively user-friendly tools available to non-specialist researchers and practitioners [10; 11]. The core contribution of this paper is thus twofold: (1) to illustrate a generalisable model for the use of generative AI in method development in safety science; and (2) to present a practical walkthrough of a conceptual workflow workbench that can support researchers in the preliminary testing and refinement of OHS methods. In the literature on the application of AI in OHS, one fundamental study is by [PERSON] et al. (2022) [12], which outlines the use of traditional machine learning algorithms for predicting workplace accidents based on historical incident data. While this approach has proven moderately effective, it fundamentally lacks the ability to adapt to novel or evolving safety conditions, a limitation that our research addresses through the use of LLMs [12]. Moreover, at the present time (December 2024), the Scopus database does not report any document regarding OHS and Chain-of-thought concurrently. Therefore, the main outcome of this paper is to propose a structured methodology to generate specific methodologies for OHS based on Chain-of-thoughts prompting. Our workbench leverages the generative capabilities of LLMs not only to predict but also to simulate potential safety scenarios, thereby filling a critical gap in dynamic risk assessment. This method significantly extends the utility of AI in safety science, offering a more robust, adaptable framework that is capable of keeping pace with changes in workplace environments and safety protocols. Given the high stakes of safety-related research and the potential ethical constraints on data collection, this framework serves as a valuable resource for those seeking to validate the applicability and robustness of new approaches. In describing the proposed model, we detail how AI tools can be tailored to specifically support OHS applications while remaining accessible to researchers without specific training in machine learning or artificial intelligence.