Data Curation: Quality, Coverage, and Quantity
~45 min read
How to define what data you need, audit what you have, and fill coverage gaps before training.
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Key points
- •Measure accuracy, consistency, completeness, and timeliness independently — a dataset can pass three axes and fail one
- •Coverage analysis requires comparing your training distribution to your production query distribution, not just counting examples
- •For PEFT fine-tuning, 500-5,000 high-quality domain examples are often enough — quality over quantity