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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.

How the Amazon.com Catalog Team built self-learning generative AI at scale with Amazon Bedrock

The insight: Turning disagreements into opportunities