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AI-ready questions test whether you can reason about LLMs and AI systems as engineering material — limits, costs, evaluation, failure modes — not just hype.

AI-ready · Error Handling

When AI output is mostly right but has one quiet error buried in it, how do you make sure that error doesn't slip through?

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Mostly-right is the maximum-danger case — quality high enough to disarm review, one error still inside — so the principle is that review effort scales with consequence, never with apparent quality.…

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