eval-audit

Auditing an LLM eval pipeline evaluates six diagnostic areas including error analysis, evaluator design, judge validation, human review, labeled data hygiene, and pipeline hygiene to surface problems and generate a prioritized findings report with concrete fixes.

Validating LLM judges involves calculating True Positive Rate (TPR) and True Negative Rate (TNR) metrics to verify evaluator accuracy, enforcing binary judge design, and encouraging code-based checks to ensure your evaluation system is trustworthy.

Bootstrapping error analysis with no eval infrastructure uses guidance to generate synthetic data and establish initial evaluation baselines, providing a starting point to build trustworthy evals from scratch.

Yes, you can use this audit on inherited evaluation systems, new deployments, and dashboards containing traces, evaluator configs, judge prompts, and labeled data to surface missing error analysis and other pipeline flaws.

Binary judge prompts and code-based checks are needed because they enforce clear evaluator design, reduce ambiguity in judge validation, and improve labeled data hygiene by ensuring consistent, measurable evaluation outcomes.

Unvalidated LLM eval pipelines with poor labeled data hygiene suffer from vanity metrics, missing error analysis, and unvalidated judges, producing misleading dashboard results that fail to reflect true model performance.