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Taxonomic Framework for Prompt Classification and Ranking

1. Complexity Dimensions​

1.1 Primary Classification Axes​

  1. Syntactic Complexity

    • Word count
    • Sentence structure
    • Grammatical complexity
    • Clause nesting depth
    • Modifier density

  2. Semantic Depth

    • Concept abstraction level
    • Metaphorical content
    • Contextual dependencies
    • Cultural references
    • Domain-specific terminology

  3. Contextual Integration

    • Environmental parameters
    • Temporal relationships
    • Spatial relationships
    • Causal connections
    • Narrative complexity

  4. Technical Specification

    • Parameter precision
    • Technical constraints
    • Quality requirements
    • Output format details
    • Performance metrics

2. Complexity Levels Framework​

2.1 Level 0: Basic Tags​

Complexity Score: 1.0
Structure: [Object] + [Action/State]
Example: "red car"
Features:
- Single concept
- Direct reference
- No modifiers
- No context

2.2 Level 1: Enhanced Tags​

Complexity Score: 2.0
Structure: [Object][Attributes] + [Action/State][Basic Modifier]
Example: "shiny red sports car parked"
Features:
- Multiple attributes
- Basic modifiers
- Simple relationships
- Minimal context

2.3 Level 2: Descriptive Phrases​

Complexity Score: 3.0
Structure: [Object][Attributes][Context] + [Action/State][Modifiers][Environment]
Example: "shiny red sports car parked in urban setting, evening light"
Features:
- Environmental context
- Multiple relationships
- Temporal elements
- Basic scene composition

2.4 Level 3: Contextual Sentences​

Complexity Score: 4.0
Structure: Complete sentences with multiple elements and relationships
Example: "A pristine Ferrari 488 parked outside a modernist building in downtown Miami during golden hour, reflecting the warm sunset in its polished surface"
Features:
- Specific details
- Multiple contexts
- Clear relationships
- Scene composition
- Atmospheric elements

2.5 Level 4: Complex Scenarios​

Complexity Score: 5.0
Structure: Multiple related sentences with detailed parameters
Example: "In a bustling 1960s American diner, a young couple shares a milkshake. The scene captures the quintessential mid-century aesthetic with chrome fixtures and red vinyl booths. Warm tungsten lighting creates nostalgic amber tones, while through the window, a classic Chevrolet reflects neon signage."
Features:
- Multiple subjects
- Period-specific details
- Complex relationships
- Atmospheric elements
- Cultural context

2.6 Level 5: Abstract Concepts​

Complexity Score: 6.0
Structure: Complex narrative with abstract elements and metaphorical content
Example: "Visualize the concept of time's passage through the metaphor of a tree growing in accelerated motion. The scene transitions from seedling to ancient oak, with seasonal cycles compressed into moments. Incorporate elements suggesting technological progress in the background, while maintaining an organic, dreamlike quality."
Features:
- Abstract concepts
- Metaphorical elements
- Complex transitions
- Multiple timeframes
- Philosophical undertones

3. Evaluation Metrics​

3.1 Complexity Scoring System​

Base Metrics​

1. Lexical Complexity (LC)
   - Unique words ratio: 0.0-1.0
   - Technical term density: 0.0-1.0
   - Average word length: normalized 0.0-1.0

2. Syntactic Complexity (SC)
   - Clause depth: 0.0-1.0
   - Modifier density: 0.0-1.0
   - Structure complexity: 0.0-1.0

3. Semantic Complexity (SMC)
   - Concept abstraction: 0.0-1.0
   - Reference depth: 0.0-1.0
   - Context integration: 0.0-1.0

4. Technical Precision (TP)
   - Parameter specificity: 0.0-1.0
   - Constraint clarity: 0.0-1.0
   - Quality requirements: 0.0-1.0

Composite Score Calculation​

def calculate_complexity_score(prompt):
    base_score = (
        (LC * 0.25) +
        (SC * 0.25) +
        (SMC * 0.25) +
        (TP * 0.25)
    )

    modifiers = {
        'abstract_concepts': 0.2,
        'temporal_complexity': 0.15,
        'cultural_references': 0.1,
        'technical_specificity': 0.15
    }

    final_score = base_score * (1 + sum(
        modifier * value
        for modifier, value in modifiers.items()
    ))

    return normalize_score(final_score)

3.2 Additional Ranking Criteria​

1. Execution Difficulty​

Levels:
1. Basic (1.0): Single-step generation
2. Moderate (2.0): Multi-step processing
3. Complex (3.0): Iterative refinement
4. Advanced (4.0): Multiple condition handling
5. Expert (5.0): Complex constraint satisfaction

2. Contextual Depth​

Levels:
1. Surface (1.0): Direct references
2. Shallow (2.0): Basic relationships
3. Moderate (3.0): Multiple contexts
4. Deep (4.0): Complex relationships
5. Profound (5.0): Abstract connections

3. Technical Precision​

Levels:
1. Basic (1.0): General description
2. Detailed (2.0): Specific parameters
3. Technical (3.0): Precise specifications
4. Professional (4.0): Industry standards
5. Expert (5.0): Research-grade precision

4. Implementation Framework​

4.1 Prompt Evolution System​

Stage 1: Basic Structure​

class PromptEvolution:
    def __init__(self, base_prompt):
        self.complexity_level = 0
        self.base_prompt = base_prompt
        self.enhanced_prompts = []

    def evolve(self):
        while self.complexity_level < 5:
            self.enhance_prompt()
            self.validate_enhancement()
            self.update_complexity()

Stage 2: Enhancement Pipeline​

class EnhancementPipeline:
    def __init__(self):
        self.enhancers = {
            'lexical': LexicalEnhancer(),
            'syntactic': SyntacticEnhancer(),
            'semantic': SemanticEnhancer(),
            'technical': TechnicalEnhancer()
        }

    def enhance_prompt(self, prompt, target_level):
        enhanced = prompt
        for enhancer in self.enhancers.values():
            enhanced = enhancer.apply(
                enhanced,
                target_level
            )
        return enhanced

4.2 Validation System​

Quality Metrics​

class PromptValidator:
    def validate_prompt(self, prompt):
        scores = {
            'complexity': self.measure_complexity(prompt),
            'clarity': self.assess_clarity(prompt),
            'specificity': self.evaluate_specificity(prompt),
            'consistency': self.check_consistency(prompt)
        }
        return self.compute_final_score(scores)

5. Application Guidelines​

5.1 Usage Recommendations​

  1. Educational Applications

    • Start with Level 0-1 for beginners
    • Gradually increase complexity
    • Focus on understanding each level's components

  2. Professional Applications

    • Begin at Level 2-3 minimum
    • Emphasize technical precision
    • Include specific quality requirements

  3. Research Applications

    • Use Level 4-5 for complex scenarios
    • Focus on abstract concept handling
    • Incorporate multiple evaluation metrics

5.2 System Evolution​

  1. Continuous Learning

    • Track successful patterns
    • Analyze failure cases
    • Update complexity metrics
    • Refine scoring system

  2. Adaptation Mechanisms

    • Model-specific adjustments
    • Domain-specific modifications
    • User-level customization
    • Performance optimization

6. Conclusion​

This taxonomic framework provides:

  • Clear complexity classification
  • Objective evaluation metrics
  • Systematic evolution paths
  • Practical implementation guidelines

Success factors:

  1. Consistent application
  2. Regular validation
  3. Continuous refinement
  4. Adaptive implementation
  5. User-specific customization

The system should evolve based on:

  • Usage patterns
  • Success metrics
  • User feedback
  • Technical advances
  • Domain requirements