| name | behavioral-consistency |
| description | Ensuring the AI behaves predictably across sessions, edge cases, and modalities. |
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
Consistency vs. Adaptation
Consistency doesn't mean rigidity. The AI should adapt to:
- User preferences (if designed for personalisation)
- Contextual needs (tone shifts as discussed in tone-calibration)
- Learning from feedback (if memory systems exist)
The key is that adaptation should be predictable and explainable, not random.
Design Artefacts
- Behavioral specification documents
- Golden response libraries
- Regression test suites
- Consistency monitoring dashboards
- Adaptation rules (what changes and what stays constant)
