Promptimus: Improving already good LLM prompts with zero manual engineering
Amazon Science Homepageby Zhengyuan Shen; Yunfei Bai; Sullam Jeoung; Shuai Wang·14 May 2026
Large language models (LLMs) have become integral to enterprise applications across industries. Under the hood, customers’ inputs to the models are usually augmented with prompts that encode...
Navigating uncertainty in Amazon's middle-mile network
Amazon Science Homepageby Ruth Misener; Hana Ku; Georgios Paschos·6 May 2026
Before the "last mile" delivery driver sets off for your home, your Amazon item has moved through the middle-mile network of fulfillment centers and sort centers, which brings products close enough...
How mechanism design theory helps optimize Amazon-vendor collaboration
Amazon Science Homepageby Dirk Bergemann·5 May 2026
When Amazon places a purchase order with a vendor, a deceptively simple question arises: how many units should go to which fulfillment center, and when?
Building trust into AI
Amazon Science Homepageby Staff writer·4 May 2026
At Amazon, AI now touches everything from warehouse logistics to customer service chatbots to AWS cloud services used by thousands of enterprises, making it a business-critical technology.
Preserving the privacy of AI training data
Amazon Science Homepageby Parker Newton; Raj Copparapu·29 Apr 2026
Large language models, the highest-profile machine learning (ML) models used today, are trained on huge corpora of public data.
How catastrophic is your LLM?
Amazon Science Homepageby Qian Hu; Weitong Ruan; Rahul Gupta·27 Apr 2026
As large language models (LLMs) become increasingly useful across a variety of domains, the stakes of keeping them safe rise accordingly.
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