Shipping an AI column is only the first step. Ongoing quality depends on clear monitoring of confidence, variance, and retry behavior.
Track output drift by sampling results per prompt segment and per input cohort. If quality drops in one segment, you can adjust instructions without rewriting the full system.
Monitor cost and latency in the same dashboard as quality metrics. This makes tradeoffs explicit when adjusting model choice or prompt depth.
When AI operations are observable, teams can iterate safely instead of treating model behavior as a black box.