upgrade-evals

To find AI failures, perform systematic error analysis on LLM pipeline traces by sampling dozens to hundreds of traces, labeling pass/fail, clustering into failure categories, and prioritizing root causes. This replaces intuition with quantified failure rates.

Categorize and prioritize AI errors by iteratively clustering labeled pipeline traces into actionable failure categories, computing failure rates per category, and recommending fixes or evaluators based on impact. This requires a PM to judge pass/fail outcomes.

You need dozens to hundreds of production or staging traces for effective LLM error analysis. This scale ensures representative sampling for accurate pass/fail labeling, meaningful failure clustering, and reliable root-cause prioritization.

Yes, you need tooling to aggregate labels and compute failure rates, plus a PM to judge pass/fail. Access to inputs, intermediate tool calls, and outputs is also required to perform root-cause prioritization on LLM pipeline traces.

Use systematic trace analysis instead of predefined categories when you need to discover emergent failure modes in AI features. It helps product managers and engineers quantify real issues from actual pipeline traces and direct fixes accurately.