AI Interface Design
AI needs an interface people can understand and control.
AI interface design is not limited to a chat box. It includes the way people provide instructions, understand system activity, review generated output, verify sources, correct mistakes, approve actions, and recover from errors.
Design an AI Product ExperienceAI product context
AI features fail in the interface more often than in the model. Users cannot tell what the system can do, what it is doing, or whether to trust what it produced. Interface design closes those gaps.
Input patterns, guided prompts, and suggested actions
Open text boxes intimidate and mislead. We design structured inputs, guided prompts, and suggested actions that show users what the system can do and lead them to good outcomes.
Processing states
When AI works, users wait. We design processing states that communicate activity, progress, and expected duration honestly — including what to do when generation takes longer than expected.
Output presentation, sources, and citations
Generated output is designed for evaluation, not just display: clear structure, visible sources, and citations placed where users actually verify claims.
Uncertainty
Systems are not equally confident in everything they produce. We design honest uncertainty presentation so users know when to check and when to trust.
Editing, regeneration, and version history
Users need to refine output, not just accept or reject it. We design editing flows, targeted regeneration, and version history so work is never lost to a retry.
Human approval and undo
Where AI takes actions, we design approval boundaries — what requires a human decision, what can proceed automatically — and undo paths for when approvals were wrong.
Feedback and error recovery
Feedback mechanisms that improve the system, and recovery paths that let users repair bad output without starting over.
Permission boundaries and trust patterns
Clear presentation of what the system can access and do on the user's behalf. Trust is a design outcome: built from transparency, control, and honest limits.
Questions about AI Interface Design
Do you design chatbots?
Sometimes — but most AI interface work is broader: generation review flows, approval boundaries, source presentation, and editing patterns inside products that are not chat-shaped at all.
Do we need a working AI feature before design starts?
No. Designing the interface early often clarifies what the AI feature should actually do, and prevents building capabilities users cannot understand.
How do you handle hallucination in the interface?
With honest design: source visibility, uncertainty presentation, verification affordances, and editing flows. The interface cannot fix a model, but it can stop users from being misled.
Can this work extend our existing product design?
Yes. AI interface patterns are usually designed as an extension of an existing product language, often alongside the Product Interface program.
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