Foundations of Data Preparation for Supervised Fine-Tuning

This article lays out the fundamental principles of data preparation for supervised fine-tuning (SFT) of AI models, asserting that the quality of input data directly dictates the upper limit of a model's potential performance. It begins by stressing the importance of rigorous quality checks to ensure data integrity and relevance before any training commences. The post then details practical aspects of data formatting, specifically recommending conversational JSONL for structured interactions. It also covers the implementation of specific schemas for reasoning and tool-calling, which are crucial for models designed to perform complex tasks or interact with external systems. Finally, the article highlights the necessity of creating a representative train/evaluation split to accurately assess model generalization and prevent overfitting.