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