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The Confidence Trap masks errors with false certainty. Our April 2026 audit of 1,324 turns confirms that multi-model review is vital. By cross-referencing OpenAI and Anthropic outputs, we reached 99.1% signal detection with only 0.9% silent turns
The "Confidence Trap" happens when we mistake an LLM’s high certainty for actual accuracy. Relying on a single output is risky
The Confidence Trap occurs when you mistake an LLM’s tone for accuracy. Relying on a single model creates blind spots. In our April 2026 audit of 1,324 turns, cross-referencing OpenAI against Anthropic proved essential. We achieved 99