Generative AI has been at the top of people’s minds lately and has been a big priority for several industries. The topic of design for Generative AI hasn’t been as widely discussed. I think design is a crucial aspect for AI developers to create frameworks and terminology to provide the best experience for users while still allowing for creative freedom.
The best analogy for AI design I can relate to is when I recently went to an art studio. This art studio had basic tools and structure, including dedicated rooms for different types of creative activities, such as one for sculpting and one for painting, and each room had tools necessary for that type of art, such as clay, paint, brushes, etc. However, how you use these tools has unlimited possibilities. Similarly, Generative AI tools can provide such foundational structure and guidance, while allowing creative freedom for users.
In this article, I want you to take away frameworks that provide structure without limiting creative freedom from using such powerful technologies.
My approach breaks down Generative AI design into three critical areas: Input Design, Iteration Support, and Output Consumption.
1. Input Design: Opening the Door to Possibilities
Clear Entry Points
- Provide examples and templates as starting points
- Show real-time suggestions based on user input
- Include “inspiration” buttons or prompts for users who need ideas
Setting Expectations
- Clearly communicate AI capabilities and limitations.
- Use progressive disclosure to reveal advanced features
- Offer contextual help that explains what’s possible in different scenarios
Guided Freedom
- Create structured input fields for specific use cases
- Allow free-form input for experienced users
- Include visual cues that suggest potential directions
2. Iteration Support: Refining the Creation
Progressive Refinement
- Help users build upon previous outputs
- Give options for adjusting parameters
- Allow for saving and comparing different versions
Smart Scoping
- Help users narrow down options without feeling restricted
- Provide filters that make sense for the context
- Show the relationship between adjustments and outcomes
Feedback Loops
- Create clear connections between input changes and results
- Enable quick undo/redo operations
- Provide ways to bookmark successful approaches
3. Output Consumption: Making Sense of Results
Adaptive Presentation
- Transform outputs into the most appropriate format (tables, graphs, maps)
- Offer multiple ways to view the same information
- Enable easy switching between presentation modes
Context Preservation
- Maintain a clear connection between inputs and outputs
- Show the “journey” of iteration that led to results
- Enable easy reference back to previous states
Action-Oriented Organization
- Group the related outputs logically
- Provide clear next steps for using the results
- Enable easy export and sharing in relevant formats
Conclusion
Generative AI interfaces should provide both structure and freedom. By focusing on input design, supportive iteration processes, and intelligent output presentation, we can create interfaces that empower users to explore and create with confidence.
We’re not just designing interfaces – we’re creating spaces for exploration and creativity. Just as an art studio provides the foundation for artistic expression, AI interfaces should provide the foundation for productive human-AI collaboration.
Remember:
- Guide without restricting
- Support exploration while preventing confusion
- Transform complex outputs into intuitive formats
- Always maintain the balance between structure and creative freedom
By following these principles, we can create AI interfaces that are powerful and accessible, enabling users to harness the full potential of generative AI technology while feeling supported and confident in their journey.
