Fine-tuning powerful language models (LLMs) has emerged as a crucial technique to adapt these systems for specific domains. Traditionally, fine-tuning relied on extensive datasets. However, Data-Centric Fine-Tuning (DCFT) presents a novel approach that shifts the focus from simply expanding dataset size to enhancing data quality and appropriateness