## 3 Proposed Workflow Workbench
### General Structure
#### 3.1.2 Synthetic Data Generation Block  
The synthetic data generation block is based on several critical considerations that are essential for overcoming the inherent challenges of data collection, particularly in areas such as OHS, where data collection can be both costly and resource intensive. Traditional methods often require extensive data collection sessions, such as semi-structured interviews, naturalistic observations, or questionnaires, all of which require a significant and sometimes impractical level of participant engagement [35]. In addition, real-world data collected using these methods are often scarce, incomplete, and subject to various types of data entry errors, thus compromising data quality. Given these limitations, it is neither practical nor advisable to rely on untested measurement methods in research campaigns, and yet a paradox remains: how can such methods be tested without reliable data? To address this issue, freely available software packages are now available that allow the generation of test data tailored to the specific needs of the research, taking into account different data types (structured or unstructured), specific probabilistic distributions, levels of respondent  
Figure 1: Diagram showing the step-by-step interaction between a human user and an LLM-based system through structured prompt engineering. Initially, in phase (**a**), “Provide framework”, the user defines a general structure for the LLM, setting broad guidelines. In phase (**b**), “Give feedback”, the LLM generates initial outputs, which the user refines through feedback, enhancing precision. Phase (**c**), “Make requests about a context”, involves the user specifying details to tailor the framework to the task’s unique needs. Finally, in phase (**d**), “Provide answers”, the LLM delivers detailed solutions aligned with both general principles and specific task requirements, illustrating a responsive and adaptive workflow that enhances the LLM’s effectiveness. The entire process can be subject to continuous feedback loops at each intermediate stage and can virtually extend indefinitely, as summarised by the curved arrows between the two agents and the dashed box, respectively. agreement (e.g., Cronbach's Alpha), and even variable rates of missing or incomplete responses within the data set matrix. Figure 2 outlines the general process for generating synthetic data, detailing each step from identifying the operating conditions of the test to implementing synthetic data in method trials. The synthetic data generation workflow is detailed through a structured, multi-stage process. The initial stage, \"Identify the test's operating conditions\", establishes the foundational conditions and requirements of the test environment (a). This involves defining the scope and constraints under which synthetic data will be utilized. Subsequently, \"Identify the typology of needed data\" determines the specific type of data necessary for the research, distinguishing between structured and unstructured formats, along with any relevant characteristics essential for accurate simulation (b). Following this, the stage titled \"Identify the experimental data parameters\" involves defining key parameters that the synthetic data must adhere to, such as distribution properties, levels of consistency among variables, and patterns of missing data (c). These parameters are critical as they guide the data generation process to ensure the synthetic data meet the experiment's specific requirements. The next step, \"Select the proper synthetic generator\", involves choosing the most suitable tool for generating the data based on the previously identified data type and parameters (d). For instance, text-based data may be generated using a language model, while statistical data may employ parametric tools or Monte Carlo simulations.