instruction dataset curation

The behaviour you fine-tune in is only as good as the examples you show. Curating an instruction dataset is the careful, unglamorous work of assembling prompts and ideal responses that actually represent what you want — diverse, correct, well-formatted, and free of the junk that teaches bad habits.

Good curation balances task variety and difficulty, removes duplicates and near-duplicates, filters out wrong or unsafe answers, and standardises formatting so the model sees consistent structure. A repeated and somewhat surprising finding is that a few thousand carefully filtered examples can beat hundreds of thousands of noisy ones — quality and diversity matter more than raw count. Human review, automated quality scoring, and decontamination against test sets are standard steps.