Inputs AI UX patterns

Input patterns shape how users prompt, attach context, select modes, and compose multimodal requests.

Essential

Clarify tools, attachments, modes, and templates before the user sends.

More in Inputs

Inputs

Smart Autocomplete

Context-aware autocomplete beyond text

Inputs

Command Bar

Jump to AI actions with a command palette

Inputs

Input Mode Toggle

Switch between text, voice, and dictation modes

Inputs

Follow-up Chips

Suggested next turns

Inputs

Context Mentions

Reference files, docs, or agents with @

Inputs

Slash Commands

Quick actions via /

Inputs

Magic Edit

Transform selection

Inputs

Voice Input

Speech-to-text with visual feedback

Inputs

AI Context Menu

AI actions on the current selection

Inputs

Predictive Type

Ghost-text completions ahead of the caret

Inputs

Tone Sliders

Adjust writing style with visible controls

Inputs

Persona Selector

Switch the assistant role for the task

Inputs

Gesture Input

Draw or gesture to trigger AI actions

Inputs

File Upload with AI Preview

Upload files with AI-generated previews

Inputs

Batch Input Processing

Process multiple inputs at once

Frequently asked questions

What problems do input patterns solve?

They make intent legible before send: attachments, mode switches, templates, and constraints that reduce malformed or ambiguous requests.

How do command bars differ from slash commands?

Command bars are global, searchable action palettes (often Cmd+K). Slash commands are in-context, usually in the composer. Use both for power users at different scopes.

When is multimodal input worth the complexity?

When your users routinely mix voice, files, and images with text, support, field apps, creative tools, not when a single text box covers 95% of jobs.

What should tool-switching UI communicate?

Which capabilities are active, how they change model behavior, and how to turn them off. Silent mode switches cause “why did it do that?” support tickets.

How do tone sliders and persona selectors help?

They set expectations before generation, output style, audience, and guardrails. So users don’t post-hoc fight the model with long correction prompts.

Which input patterns reduce bad prompts?

Templates, smart autocomplete, predictive type, and file-upload previews steer users toward valid structure; pair with empty states that show strong examples.