An order-swapped evaluation harness that removes position bias from LLM-as-judge win rates

Teams that compare prompt or model variants with an LLM judge see win rates swing wildly when they swap which option appears first, which means decisions ship on noise. A service that automatically runs both orderings, detects position bias, and reports a corrected win rate with confidence intervals would turn those noisy numbers into a trustworthy gate.

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Why this is worth a look — three verifiable signals, not a black-box score.

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