Posts tagged "observability"

A practical AI failure taxonomy that separates what happened, where it happened, its impact, and the suspected cause so teams can build useful metrics.

A practical pipeline for learning from production AI failures while minimizing personal data, preserving the failure signal, and keeping evaluation fixtures reviewable.