A custom metric was used to calculate the absolute error between the LLM predicted TPSA value and the true TPSA value, using this difference to guide prompt and example selection across bootstrap example selection and prompt optimization. During bootstrap example selection, the metric assesses the accuracy of candidate few-shot examples generated from the training dataset. A threshold-based approach was used, retaining only examples where the absolute error was below 20. This threshold ensures that the examples selected for bootstrapping are reliable representations of a good TPSA prediction, forming a solid foundation for the fewshot examples used in prompt optimization. In prompt optimization, the metric guides Bayesian Optimization by continuously measuring the accuracy of different prompt configurations. At each trial, the effectiveness of a prompt is evaluated by calculating the negative absolute error across a batch of examples. Additionally, every few minibatches the entire validation set was evaluated to confirm that the current prompt configuration performs well on a broader set of examples, enhancing stability and reducing noise in prompt selection. By calculating negative absolute error between predicted and actual TPSA values, this metric guides the optimizer towards more accurate prompt selections.