GPT-4-o-mini was used as the model for generating and testing prompts, ensuring that both prompt generation and task completion maintained consistent model behavior. GPT-4o-mini which has a reduced model size compared to GPT-4o was used here in part to minimize the risk that prior training data would contain direct answers to the questions being asked. The reduced parameter size means these direct connections are less likely. The most recent version of MIPRO, MIPROv2 from the DSPy package was used. 10 few-shot example sets were proposed during the optimization. By generating 10 sets, MIPRO can experiment with a range of examples, allowing it to assess which examples best aid the model in reducing TPSA prediction errors. An initial temperature of 1.2 was used. This increases prompt diversity at the start. This helps MIPRO to explore various prompt combinations early on, with a controlled decrease in diversity over time for convergence. 25 trials were run, allowing MIPRO to iteratively refine prompts based on validation performance. Each trial generates a new prompt set, and Bayesian Optimization identifies which sets perform best. Minibatch evaluation was performed in batches of 5 examples, enabling efficient prompt evaluation in each trial. This approach allows for broader prompt testing within the given trial limit of 30. The number of few-shot and labeled examples in each prompt was set to a maximum of 8, ensuring manageable prompt length and optimizing example diversity without overwhelming the model with too many examples at once. After every 5 minibatches, a full evaluation on the validation set was performed. This periodic full evaluation providef a more stable performance benchmark, allowing the Bayesian optimizer to adjust prompt selection based on more reliable performance data.