Chapter 10

Anti-Patterns That Create False Confidence

Name the false-confidence pattern before proposing a fix. Replace pass/fail theater with distributions, blockers, slices, and examples that explain the release decision. Avoid prompt whack-a-mole and single-bug fixes that regress other behavior. Do not treat refusal, aggregate…

Apply this chapter

Name the false-confidence pattern before proposing a fix.
Replace pass/fail theater with distributions, blockers, slices, and examples that explain the release decision.
Avoid prompt whack-a-mole and single-bug fixes that regress other behavior.
Do not treat refusal, aggregate scores, or one good demo as safety evidence.

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16 focused briefs

Concepts in this chapter

071
Anti-Patterns: The Boolean Pass/Fail TrapA single green or red result can hide the very uncertainty builders need to explain.
072
Anti-Patterns: Percent Passed Is Not QualityA 94% pass rate can be comforting, meaningless, or dangerous depending on what failed.
073
Anti-Patterns: Over-Specific Test Plans and Test CasesExact steps and exact expected words can make AI tests brittle while missing the behavior that matters.
074
Anti-Patterns: The Golden Answer ProblemMany AI tasks do not have one correct answer, and pretending they do creates bad evals.
075
Anti-Patterns: Filing Every Bad Output Like a BugOne bad AI output is usually evidence of a behavior pattern, not a single defect with a surgical fix.
076
Anti-Patterns: The Whack-a-Mole Tuning TrapPrompt patches and fine-tunes can remove one visible failure while creating quieter failures nearby.
077
Anti-Patterns: The One-Run Demo FallacyA beautiful demo proves what the system can do once, not what it will do reliably.
078
Anti-Patterns: The Static Test PlanA frozen test plan can look responsible while the AI system keeps changing underneath it.
079
Anti-Patterns: The Aggregate Score TrapOverall quality can improve while important users, languages, tasks, or risk categories get worse.
080
Anti-Patterns: Testing Only the Final AnswerFor RAG and agents, the visible answer is only the last step in a larger system.
081
Anti-Patterns: Treating the Judge as TruthLLM judges are useful evaluators, not objective measurement devices handed down from the sky.
082
Anti-Patterns: More Tests Means More ConfidenceA larger eval can still be weak if it is redundant, biased, synthetic in the same way, or disconnected from risk.
083
Anti-Patterns: Confusing Refusal with SafetyA model that refuses often is not automatically safe. It may simply be less useful.
084
Anti-Patterns: Treating AI Bugs Like UI BugsMany AI failures do not have one screen, one selector, one line of code, or one obvious owner.
085
Anti-Patterns: The Old Tester Job Title TrapTester, engineer, developer, product manager, QA analyst, SDET, test automation engineer, search quality engineer, and related roles are often…
086
Anti-Patterns: Hiring Yesterday's Tester for Tomorrow's SystemsAI quality teams need people who can build evidence systems, not just execute inherited test rituals.