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Chapter 63: Data Engineering for Fine-Tuning

High-quality data wins fine-tuning. This chapter builds a fine-tuning-data skill to design tasks, clean/validate datasets, generate synthetic data, and version everything for reproducibility.

Goals​

  • Define data quality principles for SFT datasets
  • Structure instruction/response formats for your tasks
  • Generate and validate synthetic data safely
  • Version datasets for reproducibility
  • Package the process into a reusable data-engineering skill

Lesson Progression​

  • Build the data-engineering skill
  • Data quality principles and instruction formats
  • Synthetic data generation and validation
  • Task API dataset creation and versioning
  • Capstone: production-ready dataset; finalize the skill

Outcome & Method​

You finish with a clean, versioned dataset for the Task API and a reusable data-engineering skill that feeds later fine-tuning chapters.

Prerequisites​

  • Chapters 61-62 (strategy and architecture)