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Designing for Generative AI

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AI is transforming the design landscape, reshaping user experiences in profound ways.
Designing Generative AI applications involves creating increasingly personalized
and intuitive interfaces that adapt to individual user needs, preferences, and
behaviors. Today’s AI-driven design goes beyond static interactions—interfaces
are now built to predict user needs before they arise, leading to hyperpersonalized and anticipatory experiences

Modern AI user experience (UX) design is shaped by the AI’s ability to
learn and evolve, enabling dynamic interfaces that respond in real time to
each user’s unique context. For instance, AI-powered algorithms analyze
behavior and situational data to anticipate needs—adjusting layouts,
content, and interactions even before a user makes a request.
Effective Generative AI design must balance automation with user
control.

As multimodal interfaces evolve, hands-free interactions through voice
and gesture are becoming more intuitive. AI systems must seamlessly
switch between input modes based on context and user behavior, even
adapting tone by recognizing emotional cues to foster more human-like
interactions.
Designers are now optimizing AI-driven interfaces not just for usability
and performance, but also for accessibility, emotional intelligence, and
contextual relevance.

This demands a rethinking of traditional UX principles, as user
interactions become more dynamic, intelligent, and adaptive. AI
integration in design is shifting the focus from static layouts to fluid, realtime experiences that adapt across devices and contexts.
The result is a new era of hyper-responsive, evolving digital ecosystems—
interfaces that continuously learn from and grow with every user
interaction.

Our Design Approach

At our studio, we believe designing for AI isn’t just about enabling
intelligence—it’s about making that intelligence accessible, responsible,
and relatable. We take a thoughtful, human-centered approach that
ensures AI experiences are intuitive, transparent, and aligned with user
needs—not alienating or opaque.

After all, what good is a smart system if it’s as confusing as quantum
physics on a Monday morning?

Informed by deep user research and insights from our senior design
team, here are five key principles we apply when designing AI-driven
products:

Designing with
Hallucinations in Mind

AI “hallucinations” occur when systems
generate responses that sound plausible but
are factually incorrect or misleading. As
designers, we tackle this challenge by crafting
interactions that promote transparency, user
awareness, and critical thinking. We build in
friction and feedback loops that help users
question, verify, and improve responses.

Design example:

A health assistant might clearly state, “This is
not a substitute for professional medical
advice” and include source links or confidence
indicators for each recommendation—building
trust while setting boundaries

Supporting
Rationalization and
Context

We design interfaces that empower users to
interpret and rationalize AI outputs rather
than accept them blindly. This includes
surfacing relevant data points, showing
reasoning, and enabling “why” behind “what.”

Design example:

A financial assistant doesn’t just say “invest in
X,” but explains why—citing data trends,
market sentiment, and associated risks—so
the user feels informed, not instructed.

Integrating Domain
Expertise

Collaboration with domain experts is core to
our process. Whether we’re designing for
legal, medical, or financial applications, we
ensure the AI is grounded in field-specific
knowledge and upholds industry standards

Design example:

In a legal assistant, partnering with
compliance experts helps surface
jurisdictional nuances and allows the system
to flag when a professional review is needed—
avoiding misinterpretation

Aligning with Mental
Models

We invest in understanding users’ mental
models—what they know, expect, and how
they make decisions. This allows us to design
AI systems that align with familiar behaviors,
enabling smoother onboarding and more
meaningful interactions. Equally, we help AI
systems form useful models of the user.

Design example:

By interviewing frequent online shoppers, we
learn how they browse, compare, and filter
products—then design a conversational
shopping assistant that mirrors these
patterns and preferences.

Accounting for Model
Limitations

We embed AI limitations into the experience
intentionally—not as disclaimers, but as
essential design elements. This helps set
appropriate expectations and fosters longterm trust

Design example:

A financial forecasting tool reminds users that
predictions are probabilistic, not guarantees,
and may miss recent market shifts. This
prevents overconfidence and invites user
discretion.

Want to read more?

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