  Generative AI (GenAI) has spurred the expectation of being creative, due to
its ability to generate content, yet so far, its creativity has somewhat
disappointed, because it is trained using existing data following human
intentions to generate outputs. The purpose of this paper is to explore what is
required to evolve AI from generative to creative. Based on a reinforcement
learning approach and building upon various research streams of computational
creativity, we develop a triple prompt-response-reward engineering framework to
develop the creative capability of GenAI. This framework consists of three
components: 1) a prompt model for expected creativity by developing
discriminative prompts that are objectively, individually, or socially novel,
2) a response model for observed creativity by generating surprising outputs
that are incrementally, disruptively, or radically innovative, and 3) a reward
model for improving creativity over time by incorporating feedback from the AI,
the creator/manager, and/or the customers. This framework enables the
application of GenAI for various levels of creativity strategically.
