ai-loading-ux

AI Loading UX

Design patterns for showing users what's happening while waiting for AI output.

Decision Framework

First, identify which pattern category applies:

User is waiting for...	Pattern Category	Key Goal
AI reasoning/thinking	Reasoning Display	Build trust through transparency
Multi-step task completion	Progress Steps	Show advancement toward goal
Content generation/streaming	Streaming States	Reduce perceived wait time
Background processing	Status Indicators	Confirm work is happening

Core Principles

1. The Elevator Mirror Effect

Users waiting for AI feel time pass slower. Give them something to watch/read—animated indicators reduce perceived wait time even when actual time is unchanged.

2. Progressive Disclosure

• Show condensed indicator by default ("Thinking...")
• Make details available but not forced
• Let curious users expand; don't burden everyone

3. More Transparency ≠ Better UX

Balance visibility with cognitive load. Users want answers, not reasoning—but they want to trust the answer came from good reasoning.

4. Signal Completion Clearly

Users must know when processing ends. Ambiguous end states frustrate users.

Pattern Quick Reference

Reasoning Display (Chain-of-Thought)

When AI is "thinking" through a problem. See references/reasoning-patterns.md.

Best approach (Claude-style):

• Hidden by default, expandable on demand
• Structured bullets when expanded
• Time counter or progress indicator
• Clear "done" state

Anti-patterns:

• Wall of streaming text (overwhelming)
• Scrolling too fast to read
• No expand option (feels opaque)
• No clear end state

Progress Steps

When AI completes sequential tasks. See references/progress-patterns.md.

Best approach:

• Show current step + total steps
• Mark completed steps visually
• Show what's actively happening
• Allow step-level details on expand

Streaming States

When content generates token-by-token. See references/streaming-patterns.md.

Best approach:

• Typing cursor or text animation
• Smooth token appearance (not jarring)
• Skeleton for expected content shape
• "Stop generating" escape hatch

Status Indicators

When background work happens. See references/status-patterns.md.

Best approach:

• Subtle but visible animation
• Brief description of current action
• Don't block user from other actions
• Notify on completion

Implementation Checklist

When implementing any AI loading state:

1. Identify pattern category from decision framework above
2. Choose visibility level: always visible, expandable, or minimal
3. Add motion: animation reduces perceived wait (but keep it subtle)
4. Show progress: time elapsed, steps completed, or content streamed
5. Signal completion: clear visual/state change when done
6. Provide escape: stop/cancel for long operations
7. Handle errors: don't leave user in permanent loading state
8. Test on slow connections: ensure graceful degradation

Product Comparisons (Reference)

Product	Approach	Strength	Weakness
Claude	Hidden reasoning, expandable, structured bullets	Low cognitive load	Can feel opaque
ChatGPT	Brief labels, auto-collapse	Unobtrusive	Less transparent
DeepSeek	Full streaming reasoning	Maximum transparency	Overwhelming
Gemini	User-scrolled, numbered steps	Clear structure	Unclear completion

Usage

Read the relevant reference file for your pattern category:

• references/reasoning-patterns.md - Chain-of-thought, thinking indicators
• references/progress-patterns.md - Step sequences, task completion
• references/streaming-patterns.md - Token streaming, content generation
• references/status-patterns.md - Background processing, polling states