error-analysis

To identify failure modes in an LLM pipeline, collect ~100 representative traces, judge pass/fail for each, group similar failures into categories, and compute failure rates to prioritize remediation.

Perform trace analysis when starting a new eval project, after significant pipeline changes like prompt rewrites or model switches, when production metrics drop, or after incidents occur.

The best way to prioritize LLM failures is to group similar trace failures into categories, compute their failure rates, and iterate to refine categories ensuring stable prioritization for remediation.

You need to collect approximately 100 representative traces to start labeling pass/fail outcomes and form an initial, actionable category taxonomy for failure analysis.

Yes, you can apply this repeatable workflow to new eval projects by collecting traces, reading them to label outcomes, clustering failures, and computing rates to guide remediation priorities.

Production metrics drop after prompt rewrites due to newly introduced failure modes, which you can systematically identify by reading traces, labeling outcomes, and categorizing the failures.