The evolution of user interfaces has always been a reflection of the needs and expectations of their users. In the age of artificial intelligence and increasingly complex user experiences, a new paradigm is emerging: Generative UI Patterns. These are user interface structures that not only present information but also help create it—often hand-in-hand with users. One of the most promising and widely applicable generative UI patterns can be summarized in three dynamic stages: Explain, Edit, Approve.
This pattern empowers users to interact with AI-driven content generation more meaningfully. It integrates transparency, customization, and validation into the workflow, ensuring that generated content is both relevant and aligned with user expectations. Whether used in copywriting tools, design platforms, or workflow automations, this pattern provides a bridge between AI capability and human control.
Understanding the Pattern
The “Explain, Edit, Approve” pattern consists of three iterative and interactive phases, each designed to maximize the usability and trustworthiness of automated content creation.
1. Explain
The first step is transparency. Before any content is generated, the system informs the user about what it is going to do, why it’s doing it, and what kind of output can be expected. This could involve showing input data, goals, tone settings, or user preferences.
- Purpose: Set user expectations and build trust.
- Example Components: Tooltips, modals, onboarding cards.
- Common Uses: Clarifying tone-of-voice selection in writing tools, showing selected audience demographics in ad copy generation.
At this stage, Explain ensures the AI is not a black box. It invites the user into the process, providing clarity and eliminating guesswork about what the system is trying to achieve.
2. Edit
After content is generated, the next step is the Edit phase, where users can interact with and refine the AI’s output. Instead of simply accepting a generative model’s suggestion, users are given the agency to tweak the output to fit their unique tone or objectives.
- Purpose: Turn generative suggestions into tailored results.
- Interface Patterns: Inline editors, suggestion dropdowns, modal editors.
- Common Uses: Editing AI-generated text, adjusting layouts in generated emails, fine-tuning dialogue in video scripts.
Importantly, these editing options must strike a balance. Too few controls, and the user feels constrained. Too many, and the simplicity of AI assistance is lost. Effective generative UIs provide contextual editing that adapts based on user choice and content type.
3. Approve
The final step is affirmation. Once the user has made their edits—or chosen not to—they’re asked to Approve the final version. This is the stage where commitment occurs, and the content gets published, saved, or advanced in the workflow.
- Purpose: Ensure accountability and content validation.
- Mechanisms: Confirmation buttons, checkboxes, review panels.
- Common Use Cases: Approving ad campaigns, publishing blog articles, submitting AI-generated code for deployment.
More than a simple confirmation, the Approval stage solidifies ownership of the result. It makes the user an active participant in the creative process, not a passive recipient.
Design Considerations
Implementing this pattern effectively means looking at each step from a user-experience lens. Visual hierarchies, interaction feedback, and user education play key roles. Here are some major considerations:
Progressive Disclosure
Not all users want to see complex configuration settings or detailed generator logic. By using progressive disclosure, interfaces can surface deeper controls only when needed, making the interaction simple yet powerful.
Real-time Interaction
The best generative UIs provide real-time response and active feedback. For instance, when editing text, users should see how changing tone or keywords affects the generation dynamically.
Error Recovery and Exploration
Users should always be able to undo decisions or regenerate alternate outputs. Interfaces that encourage exploration cultivate user confidence and satisfaction.
Accessibility and Inclusivity
The design elements of Explain, Edit, Approve must work for users with varied accessibility needs. Tooltips, voice commands, and contrast-friendly visuals can ensure a broader user base benefits from generative UIs.
Real-World Applications
This pattern isn’t theoretical—it’s actively shaping modern software in various industries. Some prominent examples include:
- Email Marketing Tools: Platforms like Mailchimp are using AI to generate subject lines and body content. Users see suggestions, edit them, and then approve before sending.
- Social Media Planners: Tools like Buffer use generative UIs to help users create engaging posts. The Explain phase shows post performance expectations, followed by customizable content and final approval steps.
- Developer Platforms: GitHub Copilot presents suggested code snippets users can edit before committing to their repository—a clear Edit and Approve structure in practice.
These systems do more than just generate—they collaborate. That’s the power of thoughtfully implemented generative UI patterns.
The Future of Generative UIs
As AI becomes further integrated into everyday tools, the need for frameworks like Explain, Edit, Approve will only increase. These patterns ensure harmony between machine-generated suggestions and human intention. They give users the confidence to utilize AI without relinquishing control or creativity.
Ultimately, tomorrow’s best user experiences will be forged in partnership—where AI handles the heavy lifting and users remain in the driver’s seat.
FAQ
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What is a generative UI pattern?
A generative UI pattern is a design model that allows users to interact with and guide AI-generated content within a user interface. -
Why is the Explain, Edit, Approve pattern important?
It improves user trust, enhances content quality, and increases user control over AI-generated material. -
Can this pattern be applied outside of content writing?
Yes, it’s also effective in design tools, data visualization, software development, and customer support workflows. -
How does this pattern improve transparency?
The Explain phase discloses how and why the AI is creating content, helping users understand and trust the process. -
Is user training required to engage with these patterns?
While initial onboarding may be helpful, well-designed versions of this pattern minimize the need for extensive training through intuitive UI and thoughtful tooling.