Error Recovery & Graceful Degradation
Problem
AI systems inevitably make mistakes or encounter situations they cannot handle, potentially frustrating users.
Solution
Design graceful degradation mechanisms and clear recovery paths that maintain user trust when AI fails.
Examples in the Wild
Google Assistant Fallbacks
When voice recognition fails, the system offers alternative input methods and clarification prompts.
Interactive Code Example
Error Recovery & Graceful Degradation Interactive Demo
This React component demonstrates error recovery & graceful degradation with practical implementation following best practices for user experience and accessibility.
Toggle to code view to see the implementation details.
Implementation & Considerations
Implementation Guidelines
Provide clear indicators when AI confidence is low or uncertain
Offer multiple recovery options when primary AI solutions fail
Make error states informative rather than just displaying generic messages
Allow users to easily bypass AI and use manual alternatives
Learn from failures to improve future AI performance
Design Considerations
Maintain user trust even when AI systems make mistakes
Ensure critical functions have reliable non-AI backup options
Provide appropriate user education about AI limitations
Design recovery flows that don't frustrate or confuse users
Consider the safety implications of AI failures in critical applications