Originally published on AI Tech Connect.
What you need to work out first There is a familiar sequence in production LLM work. Something goes wrong, someone reads a paper about self-consistency or debate or LLM-as-a-judge, and by the end of the fortnight there is a critic pass wired into the hot path. The accuracy numbers on the internal eval move a little. The bill moves a lot. Nobody ever revisits it, because unwiring a safety measure feels like an admission. The mistake is not adding verification. It is adding it without an answer to the only question that matters: does this pattern, on this route, remove more expected loss than it adds in cost and delay? That question has a shape, and the shape does not change when the models change. Four things go into it, and you can measure all four on your own traffic in an afternoon: How…
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