  While there is significant interest in using generative AI tools as
general-purpose models for specific ML applications, discriminative models are
much more widely deployed currently. One of the key shortcomings of these
discriminative AI tools that have been already deployed is that they are not
adaptable and user-friendly compared to generative AI tools (e.g., GPT4, Stable
Diffusion, Bard, etc.), where a non-expert user can iteratively refine model
inputs and give real-time feedback that can be accounted for immediately,
allowing users to build trust from the start. Inspired by this emerging
collaborative workflow, we develop a new system architecture that enables users
to work with discriminative models (such as for object detection, sentiment
classification, etc.) in a fashion similar to generative AI tools, where they
can easily provide immediate feedback as well as adapt the deployed models as
desired. Our approach has implications on improving trust, user-friendliness,
and adaptability of these versatile but traditional prediction models.
