Prototype.ai is an innovative platform that uses AI to generate customized Figma frames for UI/UX designers and consultants. This tool transforms text inputs from the user into design frames that can be used in Figma. The platform offers a variety of options that meet user-specified style guides and design preferences (e.g., layout variations, and color themes). By streamlining the design process behind prototyping high-fidelity models, Prototype.ai allows designers to prototype at a faster rate while refining user interfaces as they progress. Additionally, the platform also allows UI/UX designers to create diverse user personas to meet the requirements associated with focusing on specific target groups.
Name: Anshul Dadhwal Student number: 47386017
Protoype.ai will have the following functionalities -
Design Generation: Generating various Figma frames from text inputs. This includes having different design elements that make up a Figma frame. This can be done using existing Dall-E models offered by OpenAI.
Customization: Allowing the user to have design preferences like mood boards and style guides incorporated into their generated design.
Design Specificity: Aligning designs to fit under different user personas to meet the user experience requirements associated with prototyping.
Layout Variations: Giving users the option to select and build complex layouts, each different from the other. These layouts will have responsive frames with connection instructions for seamless presentation on Figma.
Element Libraries: Custom library for users with both pre-built and community-based elements like buttons, icons, and other UI components.
Version Control: Similar to GitHub, having version control functionalities to track design iterations throughout several sprints and being able to revert to those previous iterations if needed.
The MVP should be able to generate basic Figma frames from text inputs. The primary focus should be on the UI components and ensuring that the generated designs are meeting the minimum requirements of style guides. In addition to generating frames based on text inputs, it should generate desired UI elements (buttons, cards, navigation bars, etc.) based on text and generate multiple variations of it for options. Additionally, the MVP should be able to import their design style guide (colors, fonts, etc.) so the generated frames automatically generate according to their brand identity.
The quality attributes that must be met to ensure the success of the project are as follows-
Extensibility: The application should be designed for easier expansion in the future where it can accommodate new features and design trends. This includes the seamless integration of features across the application.
Usability: The application should have an intuitive user interface that simplifies the design process for everyone, from beginners to working professionals. This is vital to the overall success of the application as the automation of the workflow using AI should allow the users to focus on the creative aspect of the project.
Scalability: Capable of handling a growing number of users and complex design tasks without compromising performance. This includes fast prompt generation speeds during peak server traffic catering to multiple concurrent users. The application should scale economically, keeping the cost as low as possible while ensuring the performance isn’t affected.
To evaluate the MVP, conducting user tests to validate the platform’s ability to accurately translate text prompts into functional design frames is vital. Additionally, we should also measure performance metrics such as generation speed, design accuracy, and user satisfaction to ensure the platform meets the high standards required by UI/UX designers.
To test the extensibility of the application, we can test the modularity of the code by doing unit and headless testing. This will ensure that new features can be added to the code without disrupting the functionality of existing features.
To test the usability of the application, we should carry out user testing with diverse groups to check the ease of use, intuitiveness, and satisfaction of the application.
To test the scalability of the application, we can stress test the application to check how well it can handle concurrent users on the platform. Additionally, we can develop test cases for how efficiently resources are provisioned for scaling the platform based on server traffic.