AWS SFT data preparation guide
Preparing data for supervised fine-tuning Part 1: Formatting and quality
The guide provides practical SFT data preparation techniques that developers in ASEAN can implement with AWS services to improve model performance.
AWS released the first part of a two-series guide on supervised fine-tuning data preparation. The post covers quality checks, JSONL formatting for conversational data, reasoning and tool-calling schemas, and train/evaluation split methodology. This foundational content addresses data preparation as the ceiling for SFT projects.
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) formatting, reasoning and tool-calling schemas, and a representative train/evaluation split.
