Fix evaluation bias to unlock better models on Amazon Bedrock
96.8% of evaluation labels were biased toward incumbent model, blocking fair model comparison
“But this creates a circular evaluation where any alternative gets judged by how closely it matches the incumbent's behavior, not by actual correctness.”
Property Finder discovered that 96.8% of their vision AI evaluation labels were inherited from a single incumbent model rather than human-verified, making any alternative model appear to underperform by design. By rebuilding ground truth with expert annotations and running 27 rounds of evolutionary prompt optimization on Amazon Bedrock, they achieved 86% cost reduction at higher accuracy. The case surfaces a widespread but underacknowledged flaw in how teams benchmark model alternatives — circular evaluation that systematically favors the status quo.