Pit two AI models against each other for better results

Asking one AI model to check its own work fails because it repeats the same blind spots. A developer found that having a second, differently-trained model refute the first's reasoning catches errors that self-review misses. Automating this cross-check on frustration triggers shortens the error loop from four wrong answers to one.