failure-taxonomy

You can structure LLM error categories from freeform trace annotations by applying axial coding to group raw notes into coherent, non-overlapping binary failure modes. This process defines clear titles, definitions, and examples for systematic error categorization.

Axial coding in LLM evaluation pipelines is a methodology that groups open-coded trace annotations into structured, non-overlapping failure categories. It transforms unstructured review notes into an actionable taxonomy for quantifying error rates.

To quantify LLM failure modes, you apply a structured taxonomy back to your traces to re-label them. This allows you to calculate exact error rates for each category, enabling systematic prioritization of model improvements.

Yes, building a failure taxonomy requires domain expertise and iterative refinement to define binary, application-specific, and actionable failure categories from raw trace review notes.

The best way to categorize chatbot issues like misunderstandings or irrelevant responses is using axial coding to cluster raw notes into distinct categories like "Intent Misinterpretation". This creates a structured taxonomy for quantifying how often each failure occurs.