Pitting Two AI Models Against Each Other Catches Errors Better Than Self-Checking

A developer discovered that asking one AI model to actively refute another's reasoning caught errors far more reliably than re-prompting the same model repeatedly. The core problem is that a single model carries consistent blind spots, so running it multiple times only reinforces the same flawed reasoning with false confidence. The key insight was instructing the second model to find where logic breaks — not to review or agree — since models are built to be agreeable by default. The author automated this cross-model check by triggering it on signs of frustration, such as repeated complaints or stalled sessions, rather than relying on personal judgement. While this approach still allows one wrong answer through before the check fires, it significantly shortens the cycle of compounding errors compared to single-model iteration.