Data Science & Analytics
Phoenix Error Analysis
Find out what is going wrong in LLM or agent traffic by reading sampled Phoenix traces, spans, or sessions, writing free-form notes (open coding), then grouping the notes into a few narrow annotations, one per failure dimension with a small label set and counts, that pick eval targets and fix priorities (axial coding). Use for "what's going wrong with this agent", "I just instrumented my app, where do I start", "review these traces", "the chatbot keeps losing context", "what kinds of mistakes is the model making", "what categories of failures do we have", "what should I build evals for", "how do I prioritize fixes", "group these notes", "MECE breakdown" — or any framing that needs observations or categories grounded in real traces rather than invented top-down, even without naming the technique.
- Publisher / source
- Arize-ai/phoenix (found on GitHub top repositories)
- Upstream record
- Arize-ai/phoenix
- Version
- Version 1
- Published
- Publication review
- Approved for publication · recorded passed scan 2026-11-04
- Content fingerprint
9768a074b30ebe549d2da20a74941f72c369a039ddbc856bbdc614967268c966