Preparing data for supervised fine-tuning Part 1: Formatting and quality
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Data preparation determines the ceiling of any supervised fine-tuning project. This first post in a two-part series covers the foundations of SFT data prep: quality checks, conversational (JSONL) formatting, reasoning and tool-calling schemas, and a representative train/evaluation split.
Data preparation determines the ceiling of any supervised fine-tuning (SFT) project. You’ve evaluated your foundation model (FM), and out-of-the-box performance isn’t meeting your production requirements. Maybe the model doesn’t follow your output schema reliably, struggles with your domain’s classification taxonomy, or can’t maintain the tone your application demands. The question isn’t whether to customize, it’s how. This post assumes you have decided to fine-tune a foundation model and are…
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