Hold Competing Explanations for a Bad Output Spike
List and compare competing explanations for an AI-quality drop instead of locking onto the first likely cause.
When AI quality drops suddenly, the first explanation is often the easiest one to tell, not the best one to trust. ACH helps by forcing competing explanations to stay alive long enough for the evidence to discriminate between them. In AI systems, a failure spike might come from prompt changes, retrieval gaps, model changes, data shifts, or even a broken evaluation harness. If the analyst commits too early, the investigation starts proving instead of comparing. List real alternatives Name the explanations that could genuinely fit the observed pattern. Do not create fake alternatives just to look balanced; create the ones…
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