The Amazon.com Catalog is the foundation of every customer’s shopping experience—the definitive source of product information with attributes that power search, recommendations, and discovery. When a seller lists a new product, the catalog system must extract structured attributes—dimensions, materials, compatibility, and technical specifications—while generating content such as titles that match how customers search. A title isn’t a simple enumeration like color or size; it must balance seller intent, customer search behavior, and discoverability. This complexity, multiplied by millions of daily submissions, makes catalog enrichment an ideal proving ground for self-learning AI.
In this post, we demonstrate how the Amazon Catalog Team built a self-learning system that continuously improves accuracy while reducing costs at scale using Amazon Bedrock.
The challenge
In generative AI deployment environments, improving model performance calls for…
