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AI Validation Framework
title: AI Validation Framework type: guide status: stable created: 2024-02-06 tags:
- validation
- quality
- ai
- metrics complexity: advanced processing_priority: 1 semantic_relations:
- type: implements links: machine_readability
- type: extends links: validation_framework
Overview
This guide defines comprehensive validation and quality assurance frameworks for AI-oriented documentation and knowledge management.
Validation Framework
Core Metrics
# @core_metrics
validation_metrics = {
"documentation": {
"completeness": float, # Coverage of required sections
"consistency": float, # Internal consistency
"coherence": float, # Logical flow
"machine_readability": float # AI processing score
},
"knowledge_graph": {
"connectivity": float, # Graph connectivity
"coverage": float, # Concept coverage
"density": float, # Relationship density
"quality": float # Overall quality score
},
"embeddings": {
"coverage": float, # Embedding coverage
"discrimination": float, # Discriminative power
"clustering": float, # Cluster quality
"stability": float # Embedding stability
}
}
Quality Thresholds
quality_thresholds:
critical:
completeness: 1.0
consistency: 1.0
machine_readability: 1.0
standard:
completeness: 0.9
consistency: 0.9
machine_readability: 0.9
minimal:
completeness: 0.6
consistency: 0.6
machine_readability: 0.6
Validation Rules
Documentation Validation
# @documentation_validation
def validate_documentation(doc: Document) -> ValidationResult:
"""
Validate documentation quality
Validation steps:
1. Check structure completeness
2. Verify metadata consistency
3. Validate semantic markup
4. Assess machine readability
5. Verify link integrity
"""
pass
Knowledge Graph Validation
# @graph_validation
def validate_knowledge_graph(graph: KnowledgeGraph) -> ValidationResult:
"""
Validate knowledge graph quality
Validation steps:
1. Check graph connectivity
2. Verify relationship consistency
3. Validate node properties
4. Assess coverage completeness
"""
pass
Embedding Validation
# @embedding_validation
def validate_embeddings(embeddings: dict) -> ValidationResult:
"""
Validate embedding quality
Validation steps:
1. Check dimensionality
2. Verify normalization
3. Assess discrimination
4. Validate stability
"""
pass
Quality Assurance
Automated Checks
# @automated_qa
class QualityAssurance:
def check_documentation(self):
"""Documentation quality checks"""
pass
def check_knowledge_graph(self):
"""Knowledge graph quality checks"""
pass
def check_embeddings(self):
"""Embedding quality checks"""
pass
Continuous Validation
# @continuous_validation
def continuous_validation_pipeline():
"""
Continuous validation pipeline
Steps:
1. Monitor changes
2. Trigger validations
3. Generate reports
4. Update metrics
"""
pass
Processing Pipeline
Validation Flow
graph TD
A[Input] --> B[Structure Validation]
B --> C[Content Validation]
C --> D[Semantic Validation]
D --> E[Graph Validation]
E --> F[Quality Report]
Quality Monitoring
graph LR
A[Changes] --> B[Validation]
B --> C[Analysis]
C --> D[Reporting]
D --> E[Improvement]
E --> A
Integration Points
Documentation Integration
# @documentation_integration
class DocumentationValidator:
def validate(self, doc: Document) -> ValidationResult:
"""
Validate documentation
Steps:
1. Structure check
2. Content check
3. Link check
4. Quality assessment
"""
pass
Knowledge Graph Integration
# @graph_integration
class GraphValidator:
def validate(self, graph: KnowledgeGraph) -> ValidationResult:
"""
Validate knowledge graph
Steps:
1. Node validation
2. Edge validation
3. Property validation
4. Consistency check
"""
pass
Reporting Framework
Quality Reports
# @quality_reporting
def generate_quality_report() -> Report:
"""
Generate comprehensive quality report
Sections:
1. Overall metrics
2. Detailed analysis
3. Issue identification
4. Improvement suggestions
"""
pass
Metric Tracking
# @metric_tracking
def track_metrics(metrics: dict):
"""
Track quality metrics over time
Features:
1. Trend analysis
2. Regression detection
3. Progress monitoring
4. Alert generation
"""
pass
Improvement Framework
Issue Resolution
# @issue_resolution
def resolve_issues(issues: list) -> ResolutionPlan:
"""
Generate issue resolution plan
Steps:
1. Prioritize issues
2. Generate solutions
3. Plan implementation
4. Track resolution
"""
pass
Quality Enhancement
# @quality_enhancement
def enhance_quality(target: str) -> EnhancementPlan:
"""
Generate quality enhancement plan
Steps:
1. Identify opportunities
2. Propose improvements
3. Plan implementation
4. Track progress
"""
pass
Best Practices
Validation Guidelines
-
Regular Validation
- Automated daily checks
- Weekly comprehensive validation
- Monthly quality reviews
-
Quality Monitoring
- Real-time metric tracking
- Trend analysis
- Regression detection
-
Continuous Improvement
- Issue tracking
- Enhancement planning
- Progress monitoring