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 (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 evaluating how to prepare data for that work. Post-training customization provides three distinct levers. Each addresses a different gap between what the model can do today and what you need it to do. Continued pre-training (CPT) ingests large volumes of unstructured domain text to expand the model’s knowledge base. Use CPT when the model lacks familiarity with your domain’s terminology, concepts, or data patterns. Supervised fine-tuning (SFT) trains on curated input-output pairs to reshape the model’s behavior. SFT teaches the model how to respond: following instructions, adhering to schemas, adopting a specific tone, or producing structured outputs. It doesn’t inject new knowledge. It teaches the model to apply what it already knows in the way that you need, an idea sometimes called the Superficial Alignment Hypothesis. Reinforcement fine-tuning (RFT) optimizes behavior through reward signals rather than explicit demonstrations. RFT works when you can programmatically evaluate output quality but can’t easily demonstrate the reasoning path at scale. These techniques aren’t mutually …

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