## 7 EGTL Experiment  
The experiment utilizing the EGTL model was executed on the prototype of the Hyperloop Decision-Making Ecosystem (HDME), a web-based application designed to facilitate decision-making processes within Hyperloop technology projects through the creation of various simulation modeling scenarios. The HDME employs the Snowflake data warehouse for data storage, with distinct schemas established for each EGTL store: FUSION_STORE (26 tables + those generated by GenAI during EGTL process), STAGING_STORE (52 tables), and ALLIANCE_STORE (16 tables). This experimental framework integrated the EGTL model into the foundational data warehouse architecture and processing workflows within HDME. The experimental setup for the EGTL model is illustrated in Figure 5.  
Figure 5: Visualization of EGTL experiment setup in HDME solution. EGTL model’s phases are represented by a distinct color: Extract (color: purple), Generate (color: yellow), Transform (color: blue), Load (color: green). Arrows between the figure components illustrate movement of data. Experiment titles are illustrated by red color. The execution of the experiments was fully automated using Python scripts, which simulated near-real-time data processing scenarios. A range of GenAI models, including gemini-1.5-flash, gpt-3.5-turbo, gpt-4, and mistral-small, were employed at each stage of the experiment. The HDME interfaced with these GenAI models via API connections, which were also managed through Python scripting. Five distinct difficulty levels were developed to assess the applicability of GenAI models across the different stores. These levels correspond to varying degrees of data intensity exerted on the HDME system and the associated complexity for the GenAI models. Each successive level introduced increasingly complex tasks. To quantify this complexity, relative data intensity units were established, with each unit corresponding to progressively more intricate prompts given to the GenAI models, thereby demanding higher complexity in the outputs. For example, a data intensity value of two could manage twice the data volume compared to a value of one, contingent on the nature of the source data provided to the GenAI through data frames. Difficulty level 1 corresponds to a complexity of five relative data intensity units, imposing very low pressure on the HDME system. Difficulty level 2, with 10 relative intensity units, applies low pressure, while difficulty level 3, at 20 units, represents the optimal pressure needed for HDME to generate effective simulation modeling scenarios. As a result, most experiments were conducted within difficulty levels 1 through 3. However, an exception was made for the Alliance store business analysis experiment, which was conducted at difficulty level 5. This higher difficulty level was necessary because the GenAI model had to process entire tables containing comprehensive historical data across eleven Hyperloop criteria. Levels 4 and 5, apart from this case, were primarily used for extreme scenarios aimed at troubleshooting and understanding the limitations of GenAI enhancements within the ETL process. Data extraction and generation experiments within the ECTL framework were carried out in the Fusion store. The GenAI models were specifically applied during the extract phase to retrieve data for two primary tasks: Hyperloop technical specification details and advancements in Hyperloop technology. The outcomes of these data extraction experiments are presented in Table 6. The variations in average prompt and output sizes across different models and difficulty levels also highlight the impact of model architecture on handling and generating complex data.