The repository presents empirical research on downstream fine-tuning of large language models, introducing the concept of Sample Design Engineering (SDE). Through comprehensive experiments across multiple LLMs, the work investigates various sample design strategies and their effects on fine-tuning performance. The researchers develop ES-SDE, an approach that integrates effective design options, and demonstrate its superiority over baseline methods on new tasks.