--- title: How the Amazon.com Catalog Team built self-learning... description: Amazon's Catalog Team developed a self-learning generative AI system using Amazon Bedrock that continuously improves accuracy while reducing costs. The... canonical: https://daily.dev/posts/how-the-amazon-com-catalog-team-built-self-learning-generative-ai-at-scale-with-amazon-bedrock-rivfqz4w2 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: How the Amazon.com Catalog Team built self-learning generative AI at scale with Amazon Bedrock | daily.dev og:description: Amazon's Catalog Team developed a self-learning generative AI system using Amazon Bedrock that continuously improves accuracy while reducing costs. The... og:url: https://daily.dev/posts/how-the-amazon-com-catalog-team-built-self-learning-generative-ai-at-scale-with-amazon-bedrock-rivfqz4w2 og:image: https://api.daily.dev/og/posts/RivfqZ4W2.png og:image:alt: How the Amazon.com Catalog Team built self-learning generative AI at scale with Amazon Bedrock og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # How the Amazon.com Catalog Team built self-learning generative AI at scale with Amazon Bedrock **[AWS](https://daily.dev/sources/aws)** · 16 min read · 1 upvotes · 0 comments ## Summary Amazon's Catalog Team developed a self-learning generative AI system using Amazon Bedrock that continuously improves accuracy while reducing costs. The architecture uses multiple smaller models as workers that process routine cases through consensus, invoking larger supervisor models only when disagreements occur. When workers disagree, the supervisor investigates and generates reusable learnings stored in a hierarchical knowledge base, which are injected back into worker prompts to prevent similar issues. The system treats disagreements as learning signals from three sources: worker-to-worker disagreements at inference time, seller feedback through listing updates, and customer feedback through returns and reviews. This approach shifts from static model deployment to building systems that accumulate domain knowledge through production usage, with disagreement rates serving as the primary health metric. ## Full article daily.dev links to this article rather than hosting it. 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