Due to the large number of possible model configurations with all the prompt engineering techniques considered, to reduce com- putational expense, we decided to identify the most effective approach for prompt engineering with the GPT-3.5 Turbo base model before finetuning with any system prompt (Table ). Importantly, all prompt engineering techniques improved performance over using no prompt engineering and lead to similar levels of performance. This suggests that providing the model with information about ORCA inputs and guidance on what to do is essential for enabling the model to employ its limited pre-trained knowledge productively. CoT outperformed the other techniques, with a marginal improvement over ToT. Hence the former was used for all subsequent experiments. Apart from identifying the best prompt engineering technique, we also made two key observations during hyperparameter optimization. First, Fig. shows a scatter plot correlating the validation loss obtained from the OpenAI API during finetuning against the corresponding validation F1avg score. Note that the loss metric used for training GPT-3.5 Turbo is not made public by Ope- nAI. We observe significant fluctuations in validation performance over different parameter settings, often resulting in extremely low F1avg scores. This observation aligns with the findings of Yao et al. , who demonstrated that finetuning is very meticulous and can increase the tendency towards hallucinations. The plot also illustrates that a lower validation loss does not necessarily correspond to a better F1avg score. Many models achieving the best F1avg scores exhibited high losses, while, conversely, some of the worst models showed low losses. From this, we concluded that the training loss of GPT-3.5 Turbo was not a reliable metric for selecting our best model. Therefore, we decided against the validation loss for determining the best parameters for testing. Instead, we optimized the validation F1avg score directly.