AI Evaluation Metrics - Reasoning & Consistency

Definition:
Measures the AI’s ability to maintain logical coherence and consistent responses across multiple turns in a conversation.

Guide for Compliance Team and Engineers:

Purpose:
Ensure the chatbot provides reliable, logical, and non-contradictory advice throughout user interactions, critical for building trust especially in health contexts.

For Compliance Team:

  • Set Consistency Standards: Define minimum acceptable consistency rates (e.g., ≥90% for wellness, ≥98% for pharmacies).

  • Conversation Audits: Regularly review conversation logs to identify contradictions or illogical responses.

  • User Feedback: Monitor user reports of confusing or inconsistent advice.

  • Compliance Documentation: Document findings and corrective actions taken related to reasoning issues.

  • Training: Educate content reviewers and compliance staff on identifying reasoning flaws.

For Engineers:

  • Multi-turn Testing: Develop test scripts that simulate extended conversations to check for consistency.

  • Context Management: Implement robust context-tracking mechanisms to maintain state across interactions.

  • Knowledge Base Alignment: Ensure chatbot knowledge and rules are harmonized to avoid conflicting information.

  • Model Evaluation: Use specialized metrics and human-in-the-loop reviews for reasoning quality.

  • Continuous Refinement: Update models and dialogue flows based on identified inconsistencies and user feedback.

AI Compliance, AI Evaluation MetricsFrancesca Tabor