Proposal: Hard Go/No-Go Gates for Auditing Training Data Artifacts
jesusmjk · reddit · 2026-07-28
The author points out that while code, infrastructure, and model performance have strict gates in AI training pipelines, the decision to proceed with training data is still often based on scattered scripts and human judgment.
He envisions a pre-training control layer that doesn't rely on an LLM for the verdict. Instead, it uses explicit evidence (leakage, redundancy, coverage, etc.) to produce a reproducible PASS, WARNING, or FAIL status for the dataset. It could also generate repair plans and run secondary audits. Acknowledging the risks of false confidence, the author seeks feedback from engineers working with real training pipelines on whether they would trust and adopt such a blocking mechanism.
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