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🎭
AGrail Adaptive Pattern(AAP)
Lifelong adaptive safety system that dynamically generates and refines safety checks
Complexity: highSecurity & Privacy Patterns
🎯 30-Second Overview
Pattern: Lifelong adaptive safety system that dynamically generates and refines safety checks
Why: Static rules fail in evolving contexts; adaptive systems learn and improve continuously
Key Insight: Universal criteria + dual LLM validation + test-time adaptation = evolving safety
⚡ Quick Implementation
1Universal Criteria:Define base safety rules for all contexts
2Dynamic Generation:LLM-1 creates context-specific checks
3Validation Loop:LLM-2 validates and refines checks
4Test-Time Adapt:Iteratively improve during deployment
5Continuous Refine:Learn from edge cases & optimize
Example: universal_rules → check_generation → validation → adaptation → optimization
📋 Do's & Don'ts
✅Start with universal safety criteria that apply everywhere
✅Use dual LLMs for generation and validation separation
✅Support task-specific safety checks for trusted contexts
✅Track effectiveness metrics for continuous improvement
✅Implement test-time adaptation for new scenarios
❌Use single LLM for both generation and validation
❌Skip universal criteria in favor of only specific rules
❌Ignore edge cases during continuous refinement
❌Allow unlimited adaptation without boundaries
❌Forget to validate effectiveness of generated checks
🚦 When to Use
Use When
- • Evolving system requirements
- • Diverse deployment contexts
- • Continuous learning needs
- • Multi-domain applications
Avoid When
- • Static, unchanging environments
- • Single-purpose systems
- • Resource-constrained deployments
- • Regulatory freeze periods
📊 Key Metrics
Domain Coverage
Number of adapted contexts
Check Effectiveness
% threats caught by generated rules
False Positive Rate
Over-blocking reduction over time
Adaptation Speed
Hours to optimize for new domain
Rule Efficiency
Average checks per action type
System Evolution
Monthly improvement in safety metrics
💡 Top Use Cases
Multi-Domain Platform: Adapts from general assistant → healthcare → finance → education
Global Deployment: Evolves safety for different cultural contexts & regulations
Enterprise Evolution: Grows from startup MVP to enterprise-grade security
Research Systems: Adapts as capabilities expand without manual rule updates
Consumer Products: Learns from user interactions to improve safety over time
References & Further Reading
Deepen your understanding with these curated resources
Contribute to this collection
Know a great resource? Submit a pull request to add it.
🎭
AGrail Adaptive Pattern(AAP)
Lifelong adaptive safety system that dynamically generates and refines safety checks
Complexity: highSecurity & Privacy Patterns
🎯 30-Second Overview
Pattern: Lifelong adaptive safety system that dynamically generates and refines safety checks
Why: Static rules fail in evolving contexts; adaptive systems learn and improve continuously
Key Insight: Universal criteria + dual LLM validation + test-time adaptation = evolving safety
⚡ Quick Implementation
1Universal Criteria:Define base safety rules for all contexts
2Dynamic Generation:LLM-1 creates context-specific checks
3Validation Loop:LLM-2 validates and refines checks
4Test-Time Adapt:Iteratively improve during deployment
5Continuous Refine:Learn from edge cases & optimize
Example: universal_rules → check_generation → validation → adaptation → optimization
📋 Do's & Don'ts
✅Start with universal safety criteria that apply everywhere
✅Use dual LLMs for generation and validation separation
✅Support task-specific safety checks for trusted contexts
✅Track effectiveness metrics for continuous improvement
✅Implement test-time adaptation for new scenarios
❌Use single LLM for both generation and validation
❌Skip universal criteria in favor of only specific rules
❌Ignore edge cases during continuous refinement
❌Allow unlimited adaptation without boundaries
❌Forget to validate effectiveness of generated checks
🚦 When to Use
Use When
- • Evolving system requirements
- • Diverse deployment contexts
- • Continuous learning needs
- • Multi-domain applications
Avoid When
- • Static, unchanging environments
- • Single-purpose systems
- • Resource-constrained deployments
- • Regulatory freeze periods
📊 Key Metrics
Domain Coverage
Number of adapted contexts
Check Effectiveness
% threats caught by generated rules
False Positive Rate
Over-blocking reduction over time
Adaptation Speed
Hours to optimize for new domain
Rule Efficiency
Average checks per action type
System Evolution
Monthly improvement in safety metrics
💡 Top Use Cases
Multi-Domain Platform: Adapts from general assistant → healthcare → finance → education
Global Deployment: Evolves safety for different cultural contexts & regulations
Enterprise Evolution: Grows from startup MVP to enterprise-grade security
Research Systems: Adapts as capabilities expand without manual rule updates
Consumer Products: Learns from user interactions to improve safety over time
References & Further Reading
Deepen your understanding with these curated resources
Contribute to this collection
Know a great resource? Submit a pull request to add it.