  Extensive research exists on the performance of large language models on
logic-based tasks, whereas relatively little has been done on their ability to
generate creative solutions on lateral thinking tasks. The BrainTeaser shared
task tests lateral thinking and uses adversarial datasets to prevent
memorization, resulting in poor performance for out-of-the-box models. We
propose a system for iterative, chain-of-thought prompt engineering which
optimizes prompts using human evaluation. Using this shared task, we
demonstrate our system's ability to significantly improve model performance by
optimizing prompts and evaluate the input dataset.
