Use this when

An AI workflow runs at a volume where reviewing every result is impractical, but you still need ongoing assurance that quality is holding.

The pattern

AI HANDLES cases automatically
      ↓
REVIEW a sample + every flagged case
      ↓
sample looks wrong? → investigate, tighten, or pause

Instead of reviewing everything (too slow) or nothing (unsafe), a person reviews a meaningful sample of the automatic results, plus every case the workflow itself flagged.

Why it works

Once a workflow is trusted enough to act at volume, per-case human review becomes a bottleneck. Sampling keeps a human eye on quality at a sustainable cost. A representative sample reveals whether the error rate is holding, and reviewing every flagged and low-confidence case keeps attention on the risky ones. Together they catch drift before it becomes a widespread problem.

How to apply it

  1. Choose a sample size large enough to reveal real changes in quality.
  2. Always review the cases the workflow flagged or handled with low confidence.
  3. Review the sample on a regular cadence, and track the error rate over time.
  4. Feed errors found into your test set and your failed-case review.

Good fit

  • Established, high-volume workflows that have earned some autonomy.
  • Situations where full review is impossible but zero review is unsafe.

Poor fit

  • Early pilots, where you should review every case while learning.
  • High-stakes actions where every case genuinely needs approval.

What this does not solve

Sampling keeps quality visible affordably; it does not catch every individual error, and it is not a substitute for approval on high-stakes actions. Use it to monitor trusted, high-volume work, not to watch over actions that each carry serious risk.