Comprehensive Guide to Building AI Datasets: From Source Collection to Data Augmentation and Validation
This guide walks readers through every stage of building high‑quality AI training datasets—from locating open‑source data and defining goals, through collection, annotation, cleaning, large‑scale processing, optional augmentation, and splitting, to validation—using a medical QA example for fine‑tuning DeepSeek‑R1.