Section 001 · Chapter 1, The End of One-Run Testing
The Next Generation AI Builder Will Measure Uncertainty
Modern quality work is moving from checking single outputs to measuring behavior at scale, over time, and through sampling. Developers who can explain uncertainty will shape how AI systems ship.
What to do
- Define runnable checks that exercise behavior distributions, repeated runs, and sampling.
- Set acceptable outcomes and blocker failures for behavior distributions, repeated runs, and sampling before running the evaluation.
- Run representative cases for behavior distributions, repeated runs, and sampling and preserve the failures that would change the decision.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for behavior distributions, repeated runs, sampling, uncertainty needed to reproduce work on The Next Generation AI Builder Will Measure Uncertainty.
- Report results for behavior distributions, repeated runs, sampling, uncertainty by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
The main move is separating observation from inference. The sample result is what you saw. The confidence interval is what you estimate about the wider population. The release decision is a risk judgment that uses both, plus business context, severity, reversibility, and monitoring plans.
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Cite this page
Jason Arbon. "The Next Generation AI Builder Will Measure Uncertainty." Testing AI Knowledge Edition, section 1.
https://jarbon.ai/testing-ai/knowledge/ch001-measure-uncertainty.html