wind wedge
Hey builders — sharing a practical pattern we’ve been using to catch silent AI quality drift before users complain.
Infra looked healthy for us (latency/error/cost all normal), but product outcomes dropped (lower acceptance, more follow-ups, more escalations). The fix was to monitor outcomes, not just uptime.
What worked best:
Track 4 outcome metrics: completion, acceptance, follow-up loop rate, escalation ratio
Segment quickly: source / route / workflow / customer tier
Isolate one variable at a time: prompt, retrieval freshness, fallback behavior, context size
Replay a fixed benchmark set daily (consistent > perfect)
Result: earlier detection, smaller blast radius, faster recovery.
If useful, I can share our exact weekly checklist + benchmark template.
Also, if you want to try the workflow we productized: curl -fsSL https://aikeylabs.com/zh/i/dc03 | sh
