error-analysis

Trace analysis for LLM failures involves reviewing inputs, intermediate calls, tool uses, and outputs to identify where the pipeline breaks. It systematically guides you through reading traces to build a catalog of how your LLM system fails.

Prioritize fixes after pipeline changes by analyzing around 100 traces to compute failure rates. This identifies the most frequent failure modes, guiding your debugging efforts toward the highest-impact areas requiring immediate attention.

Systematically debug LLM evaluation failures by collecting production traces and applying user-driven annotation. This administrative workflow supports incident response by helping you identify, group, and categorize failure modes to address production metric drops.

Analyze approximately 100 traces to identify new failure modes. This sample size provides sufficient data for LLM-assisted clustering and failure rate computation, ensuring accurate categorization and prioritization of issues after significant pipeline changes.

Yes, you can use LLM-assisted clustering to refine failure categories. This technique groups similar failures into distinct, actionable categories, enhancing the annotation process when building a comprehensive catalog of LLM pipeline issues.