Behavioral Consistency

Users build mental models of how the AI behaves. Consistency is what makes those models reliable. Inconsistency — even if each individual response is good — erodes trust.

Dimensions of Consistency

• Across sessions: The AI should behave the same way whether it's the user's first conversation or their hundredth
• Across topics: Switching subjects shouldn't change the AI's personality or approach
• Across modalities: The AI should feel the same in chat, voice, and email
• Across users: Different users get the same quality and character (unless personalisation is designed)
• Across time: The AI shouldn't randomly change behavior after updates without user awareness

Sources of Inconsistency

• Temperature and sampling: Randomness in generation creates natural variation
• Context sensitivity: Different conversation histories lead to different behaviors
• Prompt drift: System prompts evolve over time without consistency checks
• Edge cases: Unusual inputs trigger unpredictable responses
• Model updates: New model versions may shift behavior subtly

Designing for Consistency

• Behavioral specifications: Document expected behavior for common and edge-case scenarios
• Golden responses: Maintain a library of reference responses that define the standard
• Regression testing: When anything changes, test against the golden response library
• Consistency metrics: Track behavioral variance across sessions and users
• User expectations: Set and maintain expectations about what the AI does and how