--- format: "aidr-story-markdown/v1" id: "d5da004d8354f356d591d6ffc55d4e8b85a7757c5b0515a8769d8a5c5f8e45fa" canonical_url: "https://aidr.today/d5da004d?lang=en" title: "Fix evaluation bias to unlock better models on Amazon Bedrock" lang: "en" requested_lang: "en" available_langs: ["en","vi"] translation_fallback: null fallback_fields: [] published_at: "2026-10-27T16:53:02.000Z" category: "Models" topics: ["amazon","llm"] source_urls: ["https://aws.amazon.com/blogs/machine-learning/fix-evaluation-bias-to-unlock-better-models-on-amazon-bedrock/"] summary: "Property Finder found that 96.8% of their vision-AI evaluation labels were inherited from the incumbent model, hiding its true accuracy. They built unbiased ground truth, applied evolutionary prompt optimization, and selected a more accurate, cost-effective model on Amazon Bedrock, cutting cost by 86% across 11 image-quality attributes." --- # Fix evaluation bias to unlock better models on Amazon Bedrock > [Open the canonical story]() **Published:** 2026-10-27T16:53:02.000Z **Category:** Models **Topics:** amazon, llm ## Summary Property Finder found that 96\.8% of their vision\-AI evaluation labels were inherited from the incumbent model, hiding its true accuracy\. They built unbiased ground truth, applied evolutionary prompt optimization, and selected a more accurate, cost\-effective model on Amazon Bedrock, cutting cost by 86% across 11 image\-quality attributes\. ## Sources - [Story source]()