Originally published on AI Tech Connect.
What you need to know Production traces beat every other source of training data because they carry the real distribution: the way your actual users phrase things, the failures that shipped, the edge cases nobody wrote a test for. Synthetic generation approximates this; your logs are it. The asymmetry that should worry you is that fine-tuning is not inference. At inference you scrub a single request before it leaves your network. With a hosted fine-tuning service, the provider processes your entire training dataset — the whole corpus, in bulk, once. A redaction miss at inference leaks one response. A redaction miss in training is permanent. Weights cannot be edited row by row, so the only reliable remedy is retraining the model from a clean corpus. Your terms of service almost certainly…
Top comments (0)