Whereas the base models showed little performance improvement with prompt engineering, finetuning combined with CoT yields the highest F1 scores across all three finetuning datasets. The percentage of executable files also increases across all datasets with CoT-based finetuning, with the largest increase seen in the rule-based and manual-based datasets. Notably, the manual-based finetuning dataset, when combined with CoT, achieves the highest F1 scores of all models. We explain this performance improvement by the finetuning teaching the model how to make use of the CoT prompt, learn mappings of descriptions, and dissect the different parts of an ORCA input file. Moreover, presumably the finetuned model has more knowledge about ORCA to use in its reasoning, making CoT more effective.