Preparing data for supervised fine-tuning Part 1: Formatting and quality
Your fine-tuning pipeline is only as good as your data prep. Here's the operational checklist.
Why it matters
A practitioner's guide to the unglamorous but critical work of preparing datasets for supervised fine-tuning — covering quality gates, formatting standards, and train/eval splits that determine whether your SFT project succeeds or fails.
The key facts
8 to knowTwo-part series on SFT data preparation
Covers quality checks, JSONL formatting, reasoning schemas, tool-calling schemas
Addresses train/evaluation split strategy
Published by AWS ML blog — vendor technical guidance
Focus on operational foundations, not novel capability
Covers: quality checks, JSONL conversational formatting, reasoning and tool-calling schemas
Published on AWS Machine Learning blog
Foundational guide, not a new tool or feature
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: 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.