Monitor outcomes, not only requests
Track whether the user achieved the task: resolved ticket, accepted code change, correct document answer, successful workflow, or approved creative output.
Request counts and latency are useful, but they do not prove the AI is helping. Outcome metrics connect model behavior to product value.
Watch quality and cost together
Log model choice, prompt size, retrieved context size, output length, retries, fallbacks, tool calls, and human corrections. This reveals whether quality improvements are worth their cost.
Create alerts for sudden cost spikes, abnormal fallback rates, invalid structured outputs, citation failures, and latency regressions.
Build correction loops
User corrections, reviewer edits, support escalations, and rejected generated content should feed an evaluation set. Production failures are the best source of future tests.
Review failures weekly at first. The goal is to turn incidents into durable prompts, retrieval fixes, routing rules, or product constraints.
Practical checklist
- 1Track task success and human corrections.
- 2Log model, retrieval, and tool choices.
- 3Alert on cost and fallback spikes.
- 4Convert failures into evaluation cases.
- 5Review quality drift regularly.
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