eval-audit

To audit LLM evaluation pipelines, systematically inspect observability traces, judge prompts, and metrics to identify systemic issues in error analysis, judge design, and validation processes. This diagnostic check prioritizes findings by impact to ensure production deployment readiness.

Your LLM evaluation metrics may not match real-world performance due to systemic pipeline issues like unvalidated judges, vanity metrics, or missing error analysis. Auditing observability traces and evaluator configurations helps identify and prioritize these specific reliability gaps.

To validate LLM judges and fix vanity metrics, you need access to observability traces, evaluator configurations, and human-labeled datasets. These inputs enable a diagnostic audit across six critical areas of evaluation hygiene to generate a prioritized list of actionable fixes.

Yes, you can use an audit to fix an inherited LLM evaluation system by systematically inspecting existing traces, judge prompts, and metrics. It generates a prioritized report ordered by impact, linking each identified problem to a concrete, actionable fix.

The best way to diagnose missing error analysis in AI product development is to perform a systematic audit of LLM evaluation pipelines. This process inspects observability traces and evaluator configurations to identify, prioritize, and provide actionable fixes for evaluation hygiene gaps.