Automated Prompt Evolution and Model Benchmarking System
1. Core System Architecture
1.1 Semantic Preservation Framework
Component Specifications
- Semantic Parser
class SemanticParser:
def __init__(self):
self.nlp_model = load_spacy_model()
self.concept_extractor = ConceptExtractor()
def parse(self, prompt):
return {
'entities': self.extract_entities(prompt),
'relationships': self.extract_relationships(prompt),
'attributes': self.extract_attributes(prompt),
'context': self.extract_context(prompt)
}
- Core Concept Preservation
class ConceptPreserver:
def __init__(self):
self.concept_embeddings = {}
self.relationship_graph = NetworkGraph()
def extract_core_concepts(self, parsed_data):
concepts = {
'primary': self.identify_primary_concepts(parsed_data),
'secondary': self.identify_secondary_concepts(parsed_data),
'relationships': self.map_concept_relationships(parsed_data)
}
return concepts
def verify_semantic_preservation(self, original, enhanced):
similarity = self.compute_semantic_similarity(
self.get_concept_embedding(original),
self.get_concept_embedding(enhanced)
)
return similarity > SIMILARITY_THRESHOLD
1.2 Enhancement Engine
Evolution Strategies
- Lexical Enhancement
class LexicalEnhancer:
def enhance(self, prompt, target_complexity):
enhancements = {
'vocabulary': self.upgrade_vocabulary(prompt),
'modifiers': self.add_modifiers(prompt),
'specificity': self.increase_specificity(prompt)
}
return self.compose_enhanced_prompt(enhancements)
- Structural Enhancement
class StructuralEnhancer:
def enhance(self, prompt, preserved_concepts):
return {
'base_structure': self.expand_structure(prompt),
'relationships': self.enhance_relationships(prompt),
'context': self.deepen_context(prompt)
}
- Contextual Enhancement
class ContextualEnhancer:
def enhance(self, prompt, semantic_core):
enhanced = {
'temporal': self.add_temporal_context(prompt),
'spatial': self.add_spatial_context(prompt),
'cultural': self.add_cultural_context(prompt),
'technical': self.add_technical_context(prompt)
}
return self.validate_semantic_preservation(
enhanced,
semantic_core
)
2. Automated Evolution Pipeline
2.1 Evolution Process
class PromptEvolutionPipeline:
def __init__(self):
self.semantic_parser = SemanticParser()
self.concept_preserver = ConceptPreserver()
self.enhancers = {
'lexical': LexicalEnhancer(),
'structural': StructuralEnhancer(),
'contextual': ContextualEnhancer()
}
def evolve_prompt(self, base_prompt, target_complexity):
# Extract semantic core
semantic_core = self.semantic_parser.parse(base_prompt)
preserved_concepts = self.concept_preserver.extract_core_concepts(
semantic_core
)
# Progressive enhancement
current_prompt = base_prompt
while not self.meets_complexity_target(
current_prompt,
target_complexity
):
# Apply enhancements
enhanced = self.apply_enhancements(
current_prompt,
preserved_concepts
)
# Validate semantic preservation
if self.validate_semantics(enhanced, semantic_core):
current_prompt = enhanced
else:
current_prompt = self.refine_enhancement(
enhanced,
semantic_core
)
return current_prompt
2.2 Semantic Validation
class SemanticValidator:
def __init__(self):
self.embedding_model = load_embedding_model()
self.similarity_threshold = 0.85
def validate(self, original, enhanced):
# Compare semantic embeddings
original_embedding = self.embedding_model.encode(original)
enhanced_embedding = self.embedding_model.encode(enhanced)
similarity = cosine_similarity(
original_embedding,
enhanced_embedding
)
return {
'valid': similarity > self.similarity_threshold,
'similarity_score': similarity,
'preserved_concepts': self.check_concept_preservation(
original,
enhanced
)
}
3. Model Benchmarking System
3.1 Evaluation Framework
class ModelBenchmark:
def __init__(self):
self.metrics = {
'technical_quality': TechnicalQualityMetrics(),
'semantic_accuracy': SemanticAccuracyMetrics(),
'aesthetic_quality': AestheticQualityMetrics(),
'consistency': ConsistencyMetrics()
}
def evaluate_model(self, model, test_suite):
results = {
'overall_score': 0,
'metric_scores': {},
'performance_profile': {}
}
for test_case in test_suite:
# Generate outputs
outputs = self.generate_test_outputs(model, test_case)
# Evaluate each metric
for metric_name, metric in self.metrics.items():
score = metric.evaluate(
outputs,
test_case.expected_results
)
results['metric_scores'][metric_name] = score
return self.compile_benchmark_report(results)
3.2 Test Suite Generation
class TestSuiteGenerator:
def generate_comprehensive_suite(self):
return {
'basic_prompts': self.generate_basic_tests(),
'complex_prompts': self.generate_complex_tests(),
'edge_cases': self.generate_edge_cases(),
'stress_tests': self.generate_stress_tests()
}
def generate_basic_tests(self):
# Generate simple prompt tests
pass
def generate_complex_tests(self):
# Generate advanced prompt tests
pass
def generate_edge_cases(self):
# Generate boundary condition tests
pass
4. Universal Prompt Optimization
4.1 Success Metrics
class PromptSuccessMetrics:
def __init__(self):
self.metrics = {
'generation_quality': 0.3,
'semantic_preservation': 0.3,
'model_compatibility': 0.2,
'user_satisfaction': 0.2
}
def evaluate_prompt_success(self, prompt, results):
scores = {}
for metric, weight in self.metrics.items():
scores[metric] = self.calculate_metric_score(
metric,
prompt,
results
) * weight
return sum(scores.values())
4.2 Optimization Process
class UniversalPromptOptimizer:
def optimize_prompt(self, base_prompt):
variations = self.generate_prompt_variations(base_prompt)
results = self.test_variations_across_models(variations)
return {
'optimal_prompt': self.select_best_prompt(results),
'performance_data': self.analyze_performance(results),
'optimization_path': self.document_optimization(results)
}
5. Implementation Guidelines
5.1 Best Practices
-
Semantic Preservation
- Regular validation checks
- Concept verification
- Relationship maintenance
- Context consistency
-
Enhancement Strategy
- Progressive complexity increase
- Balanced attribute addition
- Controlled vocabulary expansion
- Context enrichment
-
Quality Assurance
- Automated testing
- Human validation
- Performance monitoring
- Feedback integration
5.2 System Evolution
-
Learning Component
- Success pattern recognition
- Failure analysis
- Optimization strategy refinement
- Model-specific adaptation
-
Adaptation Mechanism
- Performance-based adjustments
- Model-specific optimization
- User feedback integration
- Continuous improvement
6. Future Developments
6.1 Advanced Features
-
Autonomous Learning
- Pattern recognition
- Success prediction
- Strategy optimization
- Self-improvement
-
Cross-Model Optimization
- Universal compatibility
- Model-specific tuning
- Performance prediction
- Adaptive strategies
6.2 Integration Capabilities
-
API Integration
- Standard interfaces
- Custom protocols
- Performance monitoring
- Error handling
-
Workflow Integration
- Pipeline automation
- Result analysis
- Report generation
- Feedback processing
7. Conclusion
This system provides:
- Automated prompt evolution
- Semantic consistency preservation
- Model benchmarking capabilities
- Universal prompt optimization
Success depends on:
- Robust semantic analysis
- Effective enhancement strategies
- Comprehensive testing
- Continuous optimization
- Adaptive learning
The system should evolve based on:
- Performance metrics
- User feedback
- Model capabilities
- Technical advances
We will focus on semantic preservation and generative model benchmarking.
I've designed a comprehensive system for automated prompt evolution with semantic preservation. Let me highlight the key innovations:
- Semantic Core Preservation:
- Concept extraction and mapping
- Relationship preservation
- Context maintenance
- Semantic validation
- Evolution Strategy:
- Progressive enhancement
- Multi-dimensional validation
- Semantic consistency checks
- Automated optimization
- Benchmarking Capabilities:
- Cross-model evaluation
- Performance metrics
- Quality assessment
- Universal compatibility
The most innovative aspects are:
- Semantic Preservation Engine:
class SemanticPreserver:
def preserve_while_evolving(self, prompt):
core_concepts = self.extract_core_concepts(prompt)
relationships = self.map_relationships(core_concepts)
enhanced = self.enhance_prompt(prompt, core_concepts)
return self.validate_preservation(enhanced, core_concepts)
- Universal Prompt Optimization:
- Learns from success patterns
- Adapts to model capabilities
- Maintains semantic consistency
- Optimizes for universal compatibility
Would you like to:
- Explore the semantic preservation algorithms in detail?
- Develop specific benchmarking metrics?
- Design test cases for the system?
- Create optimization strategies for specific models?
The system could revolutionize prompt engineering by:
- Automating optimization
- Ensuring consistency
- Enabling objective comparison
- Producing universal best practices