04
Fine-Tuning Data Preparation
Data volume guidelines, quality checklists, and the complete preparation pipeline for fine-tuning datasets.
Data Requirements & Preparation
Data quality is the single biggest determinant of fine-tuning success. "Quality over quantity" is not a cliche here — 500 high-quality examples often outperform 10,000 noisy ones.
Data Volume Guidelines
| Task Complexity | Minimum Examples | Recommended | Diminishing Returns After |
|---|---|---|---|
| Format/style transfer | 50-100 | 200-500 | ~1,000 |
| Classification (few classes) | 100-200 | 500-1,000 | ~5,000 |
| Domain-specific extraction | 200-500 | 1,000-3,000 | ~10,000 |
| Complex reasoning | 500-1,000 | 3,000-10,000 | ~50,000 |
| Code generation (domain) | 1,000-5,000 | 10,000-50,000 | ~100,000 |
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