Preparing data for supervised fine-tuning Part 1: Formatting and quality

AWS Machine LearningAugust 26, 2026en

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.

This is a short summary published by AI Global Wire. The full article is owned and hosted by AWS Machine Learning — open it there to read it in full.

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