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

Extracto de la fuente

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…