Chapter 01
The End of One-Run Testing
Replace brittle exact assertions with evaluation criteria that allow harmless variation and block harmful variation. Create repeated-run tests that measure output distributions instead of a single lucky sample. Define 10/7/4/0 scoring anchors before running the AI coding agent…
Apply this chapter
- Replace brittle exact assertions with evaluation criteria that allow harmless variation and block harmful variation.
- Create repeated-run tests that measure output distributions instead of a single lucky sample.
- Define 10/7/4/0 scoring anchors before running the AI coding agent or product workflow.
- Run a deterministic baseline first when possible, then restore production variance to isolate instability.
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Concepts in this chapter
- 001The Next Generation AI Builder Will Measure UncertaintyModern quality work is moving from checking single outputs to measuring behavior at scale, over time, and through sampling. Developers who can…
- 002What Makes a System Non-Deterministic?Before builders can evaluate unpredictable systems, they need to understand where the unpredictability comes from and which variation actually…
- 003From Exact Assertions to Evaluation CriteriaWhen outputs can vary, builders need to move from brittle expected strings to clear properties that define acceptable behavior.
- 004Scoring Quality from 0-10A numeric score gives Confidence Engineers a practical bridge between subjective judgment and measurable quality.
- 005Variance: Not All Differences Are BugsGood testing distinguishes harmless variation from variation that changes facts, safety, reliability, or user trust.
- 006DeterminismSometimes the right testing move is to turn down variation so the product, judge, or validation system becomes easier to reason about.