Recovery paths for unreliable outputs

Validation guardrails check if AI content meets requirements, but what happens when content fails these checks? Recovery paths are the helpful responses an AI system provides when it can't deliver exactly what was requested. Good recovery paths transform potential disappointments into constructive experiences. Here are practical approaches for designing them:

• Clear explanations: Tell users why their request couldn't be fulfilled exactly as asked.
• Alternative suggestions: Offer similar but acceptable options instead of just saying "no."
• Refined prompts: Suggest better ways to phrase their request.
• Confidence options: Present multiple possibilities with indicators showing reliability.
• Human escalation: Offer connection to human assistance for complex issues.

Different issues need different recovery approaches. Content with potential factual errors might include sources with a disclaimer, while potentially harmful content might be met with alternative suggestions. Recovery paths work best when they're helpful rather than just restrictive. They should guide users toward successful outcomes even when the direct path isn't available.