AI STRATEGY
Build Guardrails and Escalation Paths
Gracefully Handle Uncertainty
When your AI isn’t confident or can't answer appropriately, fallback responses protect both the user experience and system reputation. A thoughtful fallback strategy keeps the product helpful and trustworthy.
Why it's Important
Reduces user confusion or frustration
Prevents hallucinations or unsafe guesses
Maintains control over the brand experience
Signals product maturity and responsibility
Helps redirect users to support or resources
How to Implement
Define conditions for fallback use (e.g., low confidence score)
Write clear, friendly fallback message templates
Route fallbacks to human support or retry logic
Add visual cues to indicate fallback state (e.g., gray box, badge)
Test fallback scenarios with real users
Track fallback frequency and patterns
Adjust fallback behavior as confidence models evolve
Available Workshops
Fallback Message Writing Jam
User Journey Mapping with Fallback States
Fail Gracefully Simulation
Confidence Threshold Tuning Sprint
Fallback-to-Handoff Flow Review
Escalation vs. Retry Decision Tree
Deliverables
Fallback response library
Confidence threshold logic
Routing map for fallback paths
Visual fallback UX mockups
Fallback usage report per model version
How to Measure
Frequency of fallback triggers
% of sessions including fallback
User satisfaction post-fallback
Conversion or engagement drop after fallback
Accuracy of fallback trigger logic
% of fallbacks resolved via human escalation
Pro Tips
Rotate fallback copy to avoid fatigue
Let users rate fallback helpfulness
Use fallback triggers in training data selection
Highlight fallback reasons in QA reviews
Map fallback types to specific user intents
Get It Right
Keep fallback tone on-brand and reassuring
Show users you recognize the failure
Offer alternatives, not dead ends
Localize fallback copy for global users
Learn from fallback logs to improve model behavior
Don't Make These Mistakes
Using generic or vague fallback text
Failing to track fallback usage
Treating fallback as failure, not learning
Leaving users stuck after fallback
Ignoring the need for tone and clarity