eval-audit

To audit an LLM eval pipeline, run end-to-end diagnostic checks across error analysis, evaluator design, judge validation, data labeling, pipeline hygiene, and human review. This produces a prioritized findings report with structured remediation steps.

The best way to identify vanity metrics and unvalidated judges is applying structured diagnostic checks to your evaluator configs and judge prompts. This process prioritizes discovered gaps by impact and outputs a remediation-oriented governance report.

You validate existing evaluators and improve data hygiene by loading your traces, evaluator configs, and labeled data into an audit process. It enforces structured diagnostic checks and outputs a prioritized remediation plan for your AI experiments.

Yes, you can audit data quality without existing infrastructure. The audit includes a specific scenario for missing eval infrastructure, providing structured guidance on how to proceed and build trustworthy evaluation systems from scratch.

An LLM eval audit covers six core areas: error analysis, evaluator design, judge validation, data labeling, pipeline hygiene, and human review. It enforces diagnostic checks across these areas and prioritizes findings by impact for remediation.