Section 076 · Chapter 10, Anti-Patterns That Create False Confidence
Anti-Patterns: The Whack-a-Mole Tuning Trap
Prompt patches and fine-tunes can remove one visible failure while creating quieter failures nearby.
retrievalfine-tuningwhack mole tuning trap
What to do
- Define runnable checks that exercise retrieval, fine-tuning, and whack mole tuning trap.
- Set acceptable outcomes and blocker failures for retrieval, fine-tuning, and whack mole tuning trap before running the evaluation.
- Run representative cases for retrieval, fine-tuning, and whack mole tuning trap and preserve the failures that would change the decision.
Evidence to preserve
- Include the original failure, nearby cases, counterexamples, slices, and known regressions.
- Preserve the inputs, versions, configurations, raw outcomes, and results for retrieval, fine-tuning, whack mole tuning trap needed to reproduce work on Anti-Patterns: The Whack-a-Mole Tuning Trap.
- Report results for retrieval, fine-tuning, whack mole tuning trap by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
At scale, every tuning change should have a blast-radius eval: original failures, adjacent prompts, benign counterexamples, slice checks, cost and latency metrics, and holdout confirmation.
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Cite this page
Jason Arbon. "Anti-Patterns: The Whack-a-Mole Tuning Trap." Testing AI Knowledge Edition, section 76.
https://jarbon.ai/testing-ai/knowledge/ch076-whack-mole-tuning-trap.html