## 7 EGTL Experiment
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Task** & **Total** & **Succeeded** & **Avg. Model** & **Avg. Output** & **Avg. Prompt** & **Avg. Model** \\  & **Difficulty** & **Experiments** & **(\%)** & **Work Time (s)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline gemini-1.5-flash & 1 & 10 & 100.0 & 1.39 & 100 & 577.5 & 599.7 \\ gemini-1.5-flash & 2 & 10 & 100.0 & 2.27 & 100 & 566.6 & 949.9 \\ gemini-1.5-flash & 3 & 10 & 90.0 & 3.38 & 100 & 541.3 & 1663.3 \\ gpt-3.5-turbo & 1 & 10 & 100.0 & 1.74 & 100 & 577.5 & 479.3 \\ gpt-3.5-turbo & 2 & 10 & 100.0 & 6.46 & 100 & 594.3 & 987.3 \\ gpt-3.5-turbo & 3 & 10 & 60.0 & 8.00 & 100 & 360.4 & 1244.7 \\ gpt-4 & 1 & 10 & 100.0 & 6.31 & 100 & 577.5 & 490.8 \\ gpt-4 & 2 & 10 & 100.0 & 9.01 & 100 & 578.5 & 770.7 \\ gpt-4 & 3 & 10 & 100.0 & 14.54 & 100 & 578.5 & 1396.4 \\ mistral-small & 1 & 10 & 100.0 & 1.65 & 100 & 572.2 & 407.2 \\ mistral-small & 2 & 10 & 100.0 & 3.00 & 100 & 578.5 & 840.4 \\ mistral-small & 3 & 10 & 50.0 & 7.15 & 100 & 299.5 & 1476.9 \\ \hline \hline \multirow{2}{*}{Summary for all models} & Task & \multirow{2}{*}{\begin{tabular}{c} Task \\ difficulty \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Total \\ experiments \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Succeeded \\ experiments \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Avg. model \\ work time (s) \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Avg. output \\ correctness \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} Avg. prompt \\ correctness \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} Avg. model \\ super size \\ (symbols) \\ \end{tabular} } \\ \hline Difficulty level 1 & 1 & 40 & 100.0 & 2.77 & 100 & 576.28 & 494.3 \\ Difficulty level 2 & 2 & 40 & 100.0 & 5.18 & 100 & 579.5 & 887.0 \\ Difficulty level 3 & 3 & 40 & 63.8 & 8.27 & 100 & 444.9 & 1445.3 \\ \hline Total & & 120 & 87.9 & 5.41 & 100 & 533.5 & 942.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance metrics of ECTL fusion store data extraction experiment. The results reveal that all models performed exceptionally well at lower difficulty levels, with a consistent 100% success rate in difficulty levels 1 and 2. However, as the difficulty increased to level 3, the success rates for some models, particularly mistral-small and gpt-3.5-turbo, dropped significantly to 50% and 60%, respectively. Gpt-4 and gemini-1.5-flash exhibited the ability to generate larger output sizes at higher difficulty levels, which could be advantageous in scenarios requiring detailed and comprehensive data processing. The code generation by GenAI and execution in EGTL was conducted in the Fusion store. The experiment involved three tasks to improve the underlying data architecture in real time mode. GenAI generated a programming code to create new tables for missing tables related to Hyperloop subsystem specifications. The second task for GenAI was to clean up redundant Hyperloop subsystem specifications. In the scope of the third task, GenAI enhanced the existing underlying database system by creating backup tables for those previously created. All three tasks were handled by Python scripts, where one set of scripts was dedicated to code generation on flight and another set of scripts were responsible for capturing SQL queries produced by GenAI and executing them immediately. All experiments were successful, and all produced queries were correct (Table 7). Gemini-1.5-flash demonstrated the best performance of 0.95 s and 2 s for different task complexity levels with the largest output. Gpt-4 and gpt-3.5-turbo models took the longest time execution for code generation tasks. The data generation task involved populating a table related to the Hyperloop system dynamics criterion, tailored to user preferences (e.g., scenarios that are negative or highly positive). For all associated tasks, the GenAI models generated datasets in JSON format, which were then stored in the Fusion store tables.