(1)

command-based

surfacing AI

Command-based AI extends the familiar GUI paradigm - menus, sliders, forms,

and buttons - but augments it with intelligence. Here, the experience is deterministic and tool-like:

you give a clear command, the system executes it. Unlike co-creation flows,

the focus is not on open-ended exploration but on precision, efficiency, and control.

Commands might take the form of a structured input (“Summarize this document”),

a filter, or a quick action (“Remove background” in an image editor).
The paradigm shines in productivity and professional tools -

like Figma AI, Photoshop’s Generative Fill, or Excel’s AI-powered formulas - where users know what they want, and AI simply helps them get there faster.

The design challenge is to make AI enhancements feel like

natural extensions of existing interfaces, not disruptive replacements.

It sits closer to conventional UX, with AI acting

as a hidden layer of optimization rather than a creative collaborator.

The paradigm spans from simple chatbots
to sophisticated assistants like GPT, Claude, or Alexa, where the boundary between a “tool”
and a “partner” blurs /
this is what makes it a foundational piece of manyagentic
experiences as well.

The paradigm spans from simple chatbots
to sophisticated assistants like GPT, Claude, or Alexa, where the boundary between a “tool”
and a “partner” blurs /
this is what makes it a foundational piece of manyagentic
experiences as well.

Natural language becomes the interface, through back-and-forth between people and machines.
Users interact with AI through chat, voice, or multimodal dialogue, engaging in multi-turn conversations where context and memory matter.

Conversational UIs lower the barrier to entry /people can simply ask for what they need. But designing them requires solving
for ambiguity, grounding, tone, and trust.

Conversational UIs lower the barrier to entry /people can simply ask for what they need. But designing them requires solving
for ambiguity, grounding, tone, and trust.

Natural language becomes the interface, through back-and-forth between people and machines.
Users interact with AI through chat, voice, or multimodal dialogue, engaging in multi-turn conversations where context and memory matter.

Conversational UIs lower the barrier to entry /people can simply ask for what they need. But designing them requires solving
for ambiguity, grounding, tone, and trust.

The paradigm spans from simple chatbots
to sophisticated assistants like GPT, Claude, or Alexa, where the boundary between a “tool”
and a “partner” blurs /
this is what makes it a foundational piece of manyagentic
experiences as well.

The paradigm spans from simple chatbots
to sophisticated assistants like GPT, Claude, or Alexa, where the boundary between a “tool”
and a “partner” blurs /
this is what makes it a foundational piece of manyagentic
experiences as well.

Natural language becomes the interface, through back-and-forth between people and machines.
Users interact with AI through chat, voice, or multimodal dialogue, engaging in multi-turn conversations where context and memory matter.

Conversational UIs lower the barrier to entry /people can simply ask for what they need. But designing them requires solving
for ambiguity, grounding, tone, and trust.

Conversational UIs lower the barrier to entry /people can simply ask for what they need. But designing them requires solving
for ambiguity, grounding, tone, and trust.

Natural language becomes the interface, through back-and-forth between people and machines.
Users interact with AI through chat, voice, or multimodal dialogue, engaging in multi-turn conversations where context and memory matter.

Conversational UIs lower the barrier to entry /people can simply ask for what they need. But designing them requires solving
for ambiguity, grounding, tone, and trust.

Familiar, intuitive, and low-friction access to complex capabilities through dialogue, without needing to learn a new UI.

core promise

core
promise

main examples

main
examples

Chat-based UIs, voice agents (e.g., Alexa, ChatGPT), customer support bots.

“I give the system a command or example, and it instantly gives me back something useful.”

mental model

mental
model

biggest challenge

biggest
challenge

Good defaults, action clarity, and safe handling of edge cases.

Command-based Interfaces are fast, focused, and outcome-driven. They shine when users want control, speed, or creative output / but require good defaults, prompt action, and safe handling of edge cases. Command-Based AI brings intelligence into classic UI / enhancing control, speed, and efficiency without breaking established workflows.

in short

when to use
this paradigm

overview

Command-based Interfaces allow users to instruct the system to perform specific tasks or generate outputs / like rewriting text, summarizing data, or generating visuals/code. Command-Based AI Interfaces enhance familiar, GUI-driven controls (buttons, menus, filters) with intelligent execution. Users issue clear, structured instructions through established UI components / and the AI responds immediately, without iteration or conversation.

These interfaces collapse multi-step workflows into single, natural-language (or structured) prompts. They maximize efficiency and creativity, without requiring ongoing dialogue or delegation.

core promise

main examples

Figma AI (buttons like “Fix alignment” or “Rename layers”), text editing AI (”Summarize”, “Shorten”, “Rewrite"), Generative Fill in Photoshop

use

cases

bad

(1)

Outputs need iteration or refinement (use conversational instead)

(2)

Context requires explanation or exploration

(3)

Complex personalization is needed

(4)

Users need to understand "why" behind results

good

(1)

Smart filters, auto-corrections, or transformations

(2)

Predictive features that speed up manual workflows

(3)

Button-based or form-triggered AI actions (“Remove background”, “Auto-format”)

(4)

Intelligent defaults and enhancements in productivity tools

design

themes

recommen-dations

(1)

(1)

Expectation mismatch

User expects instant precision; AI may return unexpected results

(1)

(2)

Too subtle = ignored

AI enhancement must be noticeable but not disruptive

(1)

(3)

Trust without explanation

No time to explain reasoning in one-tap UIs

(1)

(4)

Over-automation

Risk of AI overriding user intent or control

(1)

(5)

Key design questions

Should the AI-enhanced version be the default or optional? How visible should confidence or uncertainty be? When should results be instant vs. require confirmation? Is this action reversible — and how quickly?

&

tooling

&

implementation

implemen-tation

notes

prototyping

(1)

Use Framer, Figma, or Lovable to simulate

(1)

button-triggered flows

(2)

Test “AI-enhanced” versions alongside traditional ones

(1)

for A/B validation

(3)

Use analytics to measure “AI click-through”

(1)

vs manual overrides

Technical Considerations

(1)

Integrate AI in isolated modules

(1)

(safe fallback to manual actions)

(2)

Use deterministic triggers

(1)

(not freeform text inputs)

(3)

Balance latency vs UX fluidity /

(1)

instant feedback is critical

(4)

Progressive enhancement / show quick baseline,

(1)

then refine (low-res > high-res image)

(5)

Run usability tests for clarity between manual

(1)

and AI-augmented actions

Team Collaboration

(1)

Work closely with engineers on micro-interactions

(1)

+ fallback logic

(2)

Collaborate on confidence thresholds

(1)

and edge-case behavior

(2)

Mental model testing

(1)

Do users understand *why* AI made choices?

(2)

Align with Data Science/ML Teams

(1)

on model performance targets and training data needs

(2)

Establish instrumentation, A/B testing, and failure analysis processes

(1)

log the right data, measure production performance, review what's not working

user

intent

archetypes

&

microcopy

examples

archetypes

&

examples

User intent archetypes

User intent archetypes

execute

“Do this task for me now.”

improve

“Improve this with one click.”

optimize

“Speed up a routine step.”

transform

“Apply a known effect or pattern to this.”

Microcopy for buttons or tools

Microcopy for buttons or tools

(1)

"Fix layout with AI”

(2)

“Summarize paragraph”

(3)

“Suggest formula”

(4)

“Optimize tone”

(5)

“Apply smart crop”

(6)

“Undo AI change”

tooltip microcopy

tooltip microcopy

(1)

“This option uses AI to improve results”

(2)

“Not happy with the output? Revert instantly”

(3)

“AI-enhanced / faster and smarter”

Ioana Teleanu is a patent-holding ai & product designer, founder, speaker, curator & creator.

she is using AI as design material to shape the future of digital products and documenting it in public.

© 2025 ai design os

Ioana Teleanu is a patent-holding ai & product designer, founder, speaker, curator & creator.

she is using AI as design material to shape the future of digital products and documenting it in public.

© 2025 ai design os

Ioana Teleanu is a patent-holding ai & product designer, founder, speaker, curator & creator.

she is using AI as design material to shape the future of digital products and documenting it in public.

© 2025 ai design os

Ioana Teleanu is a patent-holding ai & product designer, founder, speaker, curator & creator.

she is using AI as design material to shape the future of digital products and documenting it in public.

© 2025 ai design os