AWS Guide: High-Quality Data Beats Volume for SFT
AWS ML Blog · rss · 2026-08-27
The AWS ML Blog published Part 1 of their SFT data preparation guide, emphasizing that data quality sets the ceiling for fine-tuning performance. The article details key dimensions for data quality checks:
- Accuracy: Responses must be gold-standard; incorrect examples teach persistent bad habits.
- Diversity: Cover semantic breadth and information depth, including various intents, domains, difficulties, and edge cases.
- Consistency: Similar tasks should yield consistent response formats to avoid conflicting signals.
- Deduplication & Safety: Remove duplicates to prevent overfitting and scan for harmful content to avoid internalizing bias.
The post also notes that SFT is for shaping behavior (e.g., following instructions, tone) rather than injecting new knowledge (the role of CPT).
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