Generative AI is a set of algorithms, capable of generating seemingly new, realistic content and it’s primed to transform federal missions by enabling natural, user-focused interactions with agency-specific data. The most powerful generative AI algorithms are built on top of foundation models that are trained on a vast quantity of unlabeled data in a self-supervised way to identify underlying patterns for a wide range of tasks. Use of generative AI will expand productivity, improve efficiency, personalize customer experience, create new business models, content and ideas.
Collaboration, Interactive and Iterative
The core principle of our work is to allow enough flexibility and ability for people to provide feedback along the way, influence modeling, provide critical input and participate in joint iterative building of the Proof of Value that are workable for all relevant stakeholders.
Researching the problem, framing the problem(s) to be solved, and gathering enough evidence and initial direction on what to do next.
Design and Prototyping
Create several prototypes to show and test how the interface will work, including sketches, wireframes, and mockups. Interactive mockups allow us to further test the usability of designs and define design requirements for developers.
Usability and beta testing allows us to identify problems in the design, uncover opportunities to improve, and learn about the target user’s behaviors and preferences.
We define success metrics in order to evaluate the impact of our design. We utilize analytics to measure, compare, and track user behavior and engagement over time.
AI Models will be in the separate environment so that innovation and prototyping can occur without affecting systems in production.
Before engaging with any of AI modeling, we will enhance some data so that we can do better job in modeling.
Special attention will be devoted to creating AI models that can deliver immediate value for contract writing, by focusing on tightly scoped MVP.
Along with improving design system, we will create both “good” examples for design, and “bad” examples of design that can be fed into the AI model.