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Back to Mental Models
Reverse Elicitation Principle
Rather than making assumptions, AI agents should proactively ask for clarification when uncertain, reversing the typical information flow
Decision Rule
Design AI systems to ask questions rather than make potentially wrong assumptions
How It Works
Uncertainty triggers user queries, preventing downstream errors that are more expensive to fix than upfront clarification
Failure Modes
Over-asking and annoying users with trivial questions
Under-asking and making costly wrong assumptions
Not teaching the model when to ask vs when to proceed
Example Decision
“When organizing files, Claude asks 'Should I rename file without date?' rather than assuming a default behavior”