Agentic Design

Patterns
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Constitutional AI(CAI)

Training AI agents to follow constitutional principles through self-critique and improvement cycles

Complexity: highLearning and Adaptation

๐ŸŽฏ 30-Second Overview

Pattern: Train AI systems using explicitly defined principles and AI-generated feedback to achieve harmless, helpful behavior

Why: Scales oversight beyond human capacity, reduces harmful outputs, and creates transparent value-aligned AI systems

Key Insight: Constitutional principles guide AI feedback generation, creating self-supervising systems aligned with explicit values

โšก Quick Implementation

1Constitution:Define principles and behavioral constraints
2SFT:Supervised fine-tuning on helpful behaviors
3AI Feedback:Generate critiques based on constitution
4RL Training:Train preference model on AI feedback
5Validate:Test constitutional adherence and safety
Example: constitution + sft_model โ†’ ai_feedback โ†’ preference_model โ†’ constitutional_model

๐Ÿ“‹ Do's & Don'ts

โœ…Create clear, specific, and actionable constitutional principles
โœ…Use diverse constitutional principles covering multiple values
โœ…Implement iterative constitutional refinement processes
โœ…Monitor for constitutional principle conflicts and trade-offs
โœ…Validate AI feedback quality against human judgment
โœ…Test edge cases and adversarial scenarios thoroughly
โŒUse vague or contradictory constitutional principles
โŒSkip human validation of AI-generated feedback
โŒApply single constitutional framework to all domains
โŒIgnore cultural and contextual variations in values
โŒDeploy without extensive red team testing

๐Ÿšฆ When to Use

Use When

  • โ€ข Building systems requiring strong ethical alignment
  • โ€ข Reducing human annotation costs for safety training
  • โ€ข Scaling oversight to complex AI behaviors
  • โ€ข Implementing transparent value-based AI systems
  • โ€ข Creating self-regulating AI with explicit principles

Avoid When

  • โ€ข Simple tasks with clear objective metrics
  • โ€ข Domains requiring strict regulatory compliance
  • โ€ข Systems needing real-time human oversight
  • โ€ข Applications with zero tolerance for errors
  • โ€ข Contexts with highly contested moral principles

๐Ÿ“Š Key Metrics

Constitutional Adherence
% responses following defined principles
Harmlessness Rate
% outputs avoiding harmful content
Helpfulness Score
Quality of assistance provided
Value Alignment
Agreement with intended ethical framework
AI Feedback Quality
Correlation with human feedback
Robustness Score
Performance under adversarial testing

๐Ÿ’ก Top Use Cases

Conversational AI: Align chatbots with ethical principles and reduce harmful outputs
Content Moderation: Automatically enforce community guidelines and platform values
Legal AI Assistants: Ensure compliance with professional ethics and legal standards
Educational AI: Implement age-appropriate and pedagogically sound interactions
Healthcare AI: Maintain patient privacy and medical ethics in AI-assisted care
Financial AI: Ensure fair lending practices and regulatory compliance in AI decisions

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