AI Summary

Large‑language‑model‑assisted tools often skip a verification step that checks whether the model’s output is actually correct. Teams find this step tedious and time‑consuming, so they rely on the output sounding right rather than being verifiably accurate. As a result, tools pass internal reviews but fail in production when users discover the answers are wrong. The problem grows when LLM components influence real business decisions instead of just productivity accessories. The gap between intuitive correctness and ground‑truth verification is where many enterprise tools quietly break.

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