error-analysis

To analyze AI model failures, apply open coding to raw input-output traces and user feedback to generate free-form descriptions, then group them into named categories with frequency counts and percentages. This converts raw failure data into structured, prioritized error categories.

The best way to find root causes of AI failure modes is to perform axial coding on grouped failure categories, diagnose the top three categories, and generate recommended fixes. This targets the highest-impact error modes for meaningful remediation.

You need a dataset of 30 to 100 AI input-output failure examples for open coding analysis. Applying structured coding to this volume of failure traces and complaint tickets surfaces the highest-impact error modes and drives targeted evaluation design.

To design automated evals for known bad AI output examples, diagnose root causes for your top failure categories and generate specific evaluation method recommendations. This translates structured failure categories into targeted automated evals or prompt fixes.

Yes, you can use axial coding to group complaint tickets into error categories. The process converts raw failure traces and user feedback into a concise set of named categories complete with definitions, examples, and frequency counts.

For model traces, open coding produces free-form failure descriptions from individual input-output pairs, whereas axial coding groups those failures into named categories with definitions, examples, and frequency counts to prioritize remediation.