Generating synthetic data with small language models offers a cost-efficient and agile alternative to relying solely on large, resource-intensive models. Small language models can rapidly produce high-quality, domain-specific training samples with lower computational overhead, enabling quicker iteration and experimentation. Although they may not capture every nuance of larger models, their efficiency and adaptability make them ideal for supplementing datasets in specialized tasks and fine-tuning pipelines.