Section 054 · Chapter 8, Operating AI: Observability, Relevance, and Economics
Production Trace Mining
The strongest eval sets are often hiding inside production logs.
traceproduction traceproduction trace mining
What to do
- Start with privacy and governance.
- Choose cases that represent important user behavior, high risk, new failure modes, or recurring regressions.
- Keep raw traces separate from sanitized eval cases.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for trace, production trace, production trace mining needed to reproduce work on Production Trace Mining.
- Report results for trace, production trace, production trace mining by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
Production trace mining should track sampling frame, redaction method, cluster stability, label confidence, recurrence rate, severity, business impact, and whether promoted cases reduce future incident classes.
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Cite this page
Jason Arbon. "Production Trace Mining." Testing AI Knowledge Edition, section 54.
https://jarbon.ai/testing-ai/knowledge/ch054-production-trace-mining.html