QA Sampling for AI-Assisted Review (Simple Math, Defensible Outcomes)
A sampling approach you can actually run under deadline — bucketed sampling, error taxonomy, thresholds, and logging.
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The question isn’t whether AI makes mistakes.
It’s whether your workflow catches them before they become your problem.
Want the sampling plan? Download the kit.
TL;DR (quotable)
Sampling is how you make AI-assisted review defensible: define what AI is doing, define error types that matter, sample by output bucket (responsive/non-responsive/privilege/hot) instead of only overall, and use a simple, repeatable rule (fixed number or percent per batch). Randomize the sample, review with a short QA checklist, set “stop/adjust/proceed” thresholds, and log what you checked and what changed. If it isn’t logged, it didn’t happen.
The rule that keeps you honest
If it isn’t logged, it didn’t happen.
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Get the sampling plan + QA log: Download the kit.