Step 6 of 7: From Idea to Impact
An eval set isn't a QA checkbox. It's the thing that tells you whether you're allowed to trust your own feature. If you can't point to a labeled eval set with adversarial cases and a human baseline, you don't know if you're ready to ship. You're guessing.
Real-distribution sampling: pull 100-150 cases matching your actual production category mix, not a hand-picked "looks good" set.
Adversarial/edge-case sourcing: deliberately add cases designed to break the model. Aim for 20-30% of your eval set to be adversarial. A set that's all easy cases tells you nothing about production risk.
Human baseline comparison: have your best human agent resolve the same eval set blind, so "AI accuracy" isn't compared against a theoretical perfect score, but against what your actual team achieves today.
Consistency testing: run the same input through the model 3-5 times; if outputs vary meaningfully on straightforward cases, that's a signal your prompt or context isn't sufficiently constraining the task.
2. AI-Assisted Adversarial QA Prompt: the exact second-pass prompt that catches cheap, mechanical failures before a human reviewer wastes time on them.
3. Eval Set Builder: the full sizing table across common/edge/adversarial/out-of-scope categories.
4. Worked Example: which 2 of 18 adversarial cases failed on the dispute tool, and how that directly reshaped the launch design.
5. Common Pitfalls: why building an eval set only from "nice" examples is the fastest way to a false green light.
6. Nuance Most Guides Skip: why review isn't a one-time gate, but the same infrastructure you'll reuse for drift detection after launch.