Training-data review
How teams inspect training, fine-tuning, evaluation, vendor, and internal datasets before model runs.
Blog
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This is where we will publish field notes on training-data review, contamination, review queues, and dataset integrity when the notes are real enough to stand behind.
Contamination and residue
Examples of hidden instructions, leaked answer keys, refusal residue, and construction artifacts that deserve review.
Review queues
How evidence should be packaged for human review without pretending the queue is an automated verdict.
Dataset integrity
Source context, change records, review records, and the boundaries of what a pre-training data review can claim.
Design partner conversations
Reviewing training data before a model run?
We are looking for ML data and platform teams that inspect fine-tuning or eval datasets before training. Bring a dataset, a review process, or a failure mode you want surfaced earlier.