From Chat Dump to Training Set
Convert messy production traces into a supervised fine-tuning dataset with evaluation separation.
What changed between an unsafe export and a trainable dataset? Before: a log export masquerades as training data. After: demonstrations are curated and measured. Each row became a realistic input paired with an ideal output, because supervised fine-tuning imitates demonstrations. Old assistant mistakes were removed or rewritten, because training on failures teaches the failure. A validation slice stayed out of training, because improvement must be measured on examples the model did not see. Edge cases were intentionally balanced, because volume alone over-represents the easiest production path. Curate the behavior before you train the behavior. Prepare SFT data You turned logs…
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