DSPy examples serve as modular, query-answer pairs that allowed standardization of data inputs and generated a comprehensive dataset spanning a wide range of TPSA values. This dataset was balanced across TPSA intervals to prevent biases toward certain values and ensure that the LLM was exposed to a representative set of molecular features. A scaffold split was performed to ensure that the train or test sets would contain examples of any scaffolds that repeated across the data. The examples were then loaded into the LLM program as a structured dataset, where each Example provided the model with a consistent input-output relationship.