Preparing data for supervised fine-tuning Part 2: Advanced data strategies | Amazon Web Services
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...
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