아마존이 AI 품질관리 문제를 해결한 놀라운 방법 🚀

For all the enthusiasm around generative AI, a hurdle is limiting its adoption: the technology’s tendency to make things up, leave things out, and create so many possibilities that it is hard to figure out which will be effective. That’s why the vast majority of companies employ human reviews and stand-alone testing tools, but these quality-control methods are expensive, and they can handle only a fraction of gen AI’s total output. Amazon has developed a better approach for its massive product ­catalog operation: a gen AI–based system named Catalog AI that can automatically detect and block unreliable data, produce ideas for new product pages and test their effectiveness, and improve itself with feedback from quality checks and experiments. In this article Harvard Business School’s Stefan Thomke and Amazon’s Philipp Eisenhauer and Puneet Sahni describe Amazon’s system for performing quality control on AI-generated content at scale. Although Amazon considers Catalog AI to be a work in progress, the authors believe that it is far enough along that managers at other organizations can benefit from learning about it now.