Abstract: Techniques for triggering regeneration of content by a generative AI model based on changes to stored data are disclosed. A system inputs a prompt to a generative AI model to generate content based on a target data set stored at a memory location. The system monitors the target data set to detect changes to the target data set. Responsive to detecting changes to the target data set, the system triggers the generative AI model to generate updated content based on the updated version of the target data set that is currently stored at the memory location. For example, in the context of project proposal generation, when details such as scope, timelines, or budget are updated to the underlying dataset, the system automatically regenerates the proposal content using a generative AI model. This enables organizations to deliver timely, accurate, high-quality proposals to prospective customers, increasing the likelihood of winning deals.

Abstract: Techniques for generating actionable recommendations to improve program performance using generative AI are disclosed. A system invokes an application programming interface (API) service to obtain a set of program attributes. The program is made up of multiple projects that share a strategic objective. The system determines an overall performance classification for the program and a set of key program metrics that influence the overall performance classification. The system generates a prompt using the key program metrics. The system provides the prompt to a generative AI model to generate a program summary and a set of actionable recommendations to improve program performance by improving one or more of the key program metrics.