Preparing data for supervised fine-tuning Part 2: Advanced data strategies

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Data preparation for supervised fine-tuning (SFT) doesn’t end when your dataset is clean and correctly formatted. The harder questions come next. How much data do you actually need? Should you collect more, or select a better subset of what you have? How do you generate high-quality examples when human annotation doesn’t scale? And how do you specialize a model without erasing its general capabilities? This post assumes you have prepared a quality-checked, schema-compliant SFT dataset and are ready to optimize it for training. The first post in this series, Preparing data for supervised fine-tuning Part 1: Formatting and quality, covers that groundwork: the conversational format Amazon Nova expects, the quality checks that catch malformed and low-signal examples, and how to split train and evaluation data without leakage. If your dataset hasn’t been through those steps, start there. This second post covers four advanced strategies: evaluating data readiness with learning curve analysis, data subset selection and filtering, data augmentation, and data mixing. We reference Amazon Nova customization findings throughout, and the guidance applies to any model you choose. Data readiness evaluation After your data is cleaned and formatted, assess whether you have enough signal to train effectively. Volume and distribution assessment As a general starting point, plan for roughly 2,000 high-quality training samples for a typical SFT task. Treat this as a ballpark estimate: the right size varies with task difficulty and how far the model’s current behavior is from your goal. A …

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