UX design·Updated January 12, 2026

AI Error Handling UX

Design error handling patterns for AI features including error messages, recovery paths, and user guidance.

Use Case

Designing error handling for AI features, creating user-friendly error messages, or planning AI error recovery flows.

Prompt

Design error handling UX for [AI feature].

AI Feature Context:
- Feature: [name]
- AI capability: [LLM / image generation / code / etc.]
- Error types: [describe known errors]

Provide:

1. Error Taxonomy
   - Error categories (network, model, input, rate limit, etc.)
   - Error severity levels
   - User impact of each error
   - Frequency expectations

2. Error Message Design
   For each error type:
   - User-friendly message
   - Technical details (if needed)
   - What went wrong (in plain language)
   - Why it happened (if helpful)
   - What user can do

3. Visual Error States
   - Error indicators
   - Visual hierarchy
   - Color and iconography
   - Animation/motion
   - Placement and timing

4. Recovery Mechanisms
   - Retry options
   - Alternative approaches
   - Fallback behaviors
   - User actions available
   - Automatic recovery (if applicable)

5. Contextual Help
   - Helpful guidance
   - Examples of correct input
   - Tips to avoid errors
   - Links to documentation
   - Support contact (if needed)

6. Error Prevention
   - Input validation
   - Proactive warnings
   - Confirmation dialogs
   - Rate limit indicators
   - Usage guidance

7. Progressive Error Handling
   - First error: Simple message
   - Repeated errors: More guidance
   - Persistent errors: Escalation path
   - Error history: Learn from patterns

8. Trust & Transparency
   - How to maintain trust during errors
   - Transparency about AI limitations
   - Confidence indicators
   - What's happening behind scenes

9. Accessibility
   - Screen reader support
   - Keyboard navigation
   - Error announcement
   - Focus management

10. Testing Scenarios
    - Error scenarios to test
    - Edge cases
    - Recovery flows
    - User testing approach

Format as a comprehensive error handling UX specification.

How to use

1Replace [AI feature], [name], [LLM / image generation / code / etc.], and [describe known errors] with your specific details
2Add context before the prompt: Describe your AI feature and error types. Example: "Feature: Text generation. AI capability: LLM (GPT-4). Error types: Rate limits, timeouts, low confidence outputs, network errors."
3If you have existing error messages: Paste current error messages. Say "Current error messages: [paste messages]"
4If you have error scenarios: List known error scenarios. Say "Error scenarios: [list scenarios]"
5Paste the modified prompt into your preferred AI tool, like ChatGPT or Claude
6Review the error handling spec: Check error taxonomy, error message design, recovery mechanisms, and trust patterns
7Verify error messages: Ensure error messages are user-friendly and actionable
8Ask for specifics: Request "Focus on rate limit errors" or "Add more recovery mechanisms" or "Detail trust patterns"
9Export to your tool: Copy the error handling spec to Figma, Notion, or your design documentation
10Use for implementation: Apply the error handling patterns to implement user-friendly error handling

Pro Tips

• Include AI model details: Mention the AI model you're using (e.g., "GPT-4" or "Claude 3.5") so AI can provide model-specific error guidance
• Specify error types: List known error types (e.g., "Rate limits, timeouts, low confidence") so AI can design appropriate error handling
• Request user-friendly examples: Ask "Show example error messages for each error type" to see actual copy
• For trust patterns: Ask "Suggest trust patterns for AI errors" to maintain user trust during errors
• Save as template: Reuse the error handling spec structure for future AI features

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