Fix evaluation bias to unlock better models on Amazon Bedrock
aws ·
Property Finder, a digital real estate application, discovered that its model evaluation process was severely biased because 96.8% of its labels were inherited from an incumbent model's predictions rather than verified by human experts. This circular evaluation method made any alternative models appear to underperform, blocking objective comparison during a migration to Amazon Bedrock. To resolve this, the team built unbiased ground truth by having domain experts manually annotate 356 images independently of previous model outputs. They implemented an evolutionary prompt optimization loop, refining instructions based on error analysis and switching to multi-label classification to handle overlapping visual attributes. Using Llama 4 Maverick 17B Instruct with optimized prompts on Amazon Bedrock, the company achieved a Micro F1 score increase from 0.55 to 0.81 compared to their previous setup. This transition resulted in an 86% reduction in processing costs and a 40–60% decrease in inference latency while maintaining higher accuracy across all eleven quality attributes. The case study highlights the critical importance of auditing evaluation datasets for bias and using expert-annotated ground truth to ensure valid model comparisons.
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