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Preparing data for supervised fine-tuning Part 1: Formatting and quality
Data preparation determines the ceiling of any supervised fine-tuning project. This first post in a two-part series covers the foundations of SFT data prep: quality checks, conversational (JSONL) form
Data preparation determines the ceiling of any supervised fine-tuning project. This first post in a two-part series covers the foundations of SFT data prep: quality checks, conversational (JSONL) formatting, reasoning and tool-calling schemas, and a representative train/evaluation split.
Related
- Preparing data for supervised fine-tuning Part 2: Advanced data strategies
- Exploring self-distilled reasoning for supervised fine-tuning with Amazon Nova
- Nova Forge SDK series part 2: Practical guide to fine-tune Nova models using data mixing capabilities
Source: AWS ML Blog | 2026-08-26