error-analysis

To identify and categorize LLM pipeline failure modes, collect execution traces, note specific failures, group them into actionable categories, label all traces against these groups, and compute failure rates to prioritize fixes.

Analyzing LLM traces after a metric drop involves systematically reviewing recent logs, categorizing observed errors into distinct failure modes, and computing their frequencies to pinpoint the root cause for prioritized incident response.

Yes, post-change analysis is a core use case. You can evaluate an updated LLM pipeline by analyzing recent conversation traces, grouping the failures into actionable categories, and quantifying error rates to target improvements.

Computing failure rates during LLM debugging helps prioritize issues by quantifying how often each categorized failure mode occurs. This allows you to focus debugging efforts on the most impactful pipeline errors first.

Perform systematic error analysis when starting new evaluation projects, investigating metric drops, responding to incidents, or conducting post-change analysis. It requires collecting traces and grouping them into actionable categories to prioritize fixes.