# Model Development Pipelines Technical Specification

## Overview
Model development pipelines facilitate the iterative process of creating, evaluating, and optimizing AI models. These pipelines support prompt engineering, model comparison, evaluation frameworks, and fine-tuning workflows.

## Pipeline Categories

### 1. Prompt Engineering Pipelines

#### 1.1 Iterative Prompt Optimization Pipeline
**ID**: `prompt-engineering-iterative`  
**Purpose**: Systematically optimize prompts through experimentation  
**Complexity**: High  

**Workflow Steps**:
1. **Baseline Establishment** (Claude)
   - Generate initial prompt variations
   - Define success metrics
   - Create test scenarios

2. **Parallel Testing** (Parallel Claude)
   - Execute prompts across test cases
   - Collect performance metrics
   - Track token usage

3. **Performance Analysis** (Gemini)
   - Analyze results statistically
   - Identify patterns
   - Rank prompt effectiveness

4. **Prompt Refinement** (Claude Smart)
   - Generate improved variations
   - Apply learned optimizations
   - Incorporate best practices

5. **Validation** (Claude Batch)
   - Test refined prompts
   - Compare against baseline
   - Generate final report

**Configuration Example**:
```yaml
workflow:
  name: "prompt_optimization"
  description: "Iterative prompt engineering with A/B testing"
  
  defaults:
    workspace_dir: "./workspace/prompt_engineering"
    checkpoint_enabled: true
    
  steps:
    - name: "generate_variations"
      type: "claude"
      role: "prompt_engineer"
      prompt_parts:
        - type: "static"
          content: |
            Create 5 variations of this prompt for {task_type}:
            Original: {base_prompt}
            
            Focus on: clarity, specificity, and effectiveness
      options:
        output_format: "json"
        
    - name: "parallel_test"
      type: "parallel_claude"
      instances:
        - role: "tester_1"
          prompt_template: "{variation_1}"
        - role: "tester_2"
          prompt_template: "{variation_2}"
        - role: "tester_3"
          prompt_template: "{variation_3}"
      test_data: "{test_cases}"
      
    - name: "analyze_results"
      type: "gemini"
      role: "data_scientist"
      prompt: "Analyze prompt performance metrics"
      gemini_functions:
        - name: "calculate_metrics"
          description: "Calculate success metrics"
        - name: "statistical_analysis"
          description: "Perform statistical tests"
```

#### 1.2 Chain-of-Thought Prompt Builder
**ID**: `prompt-engineering-cot`  
**Purpose**: Build effective chain-of-thought prompts  
**Complexity**: Medium  

**Features**:
- Reasoning step extraction
- Example generation
- Logic validation
- Performance benchmarking

#### 1.3 Few-Shot Learning Pipeline
**ID**: `prompt-engineering-fewshot`  
**Purpose**: Optimize few-shot examples for tasks  
**Complexity**: Medium  

**Workflow Components**:
```yaml
components/prompts/few_shot_template.yaml:
  template: |
    Task: {task_description}
    
    Examples:
    {for example in examples}
    Input: {example.input}
    Output: {example.output}
    Reasoning: {example.reasoning}
    {endfor}
    
    Now apply to:
    Input: {target_input}
```

### 2. Model Evaluation Pipelines

#### 2.1 Comprehensive Model Testing Pipeline
**ID**: `model-evaluation-comprehensive`  
**Purpose**: Full evaluation suite for model performance  
**Complexity**: High  

**Evaluation Dimensions**:
1. **Accuracy Testing**
   - Task-specific benchmarks
   - Ground truth comparison
   - Error analysis

2. **Robustness Testing**
   - Edge case handling
   - Adversarial inputs
   - Stress testing

3. **Consistency Testing**
   - Response stability
   - Temporal consistency
   - Cross-prompt alignment

4. **Bias Detection**
   - Demographic parity
   - Fairness metrics
   - Representation analysis

**Implementation Pattern**:
```yaml
steps:
  - name: "prepare_test_suite"
    type: "claude"
    role: "test_designer"
    prompt: "Generate comprehensive test cases for {model_task}"
    output_file: "test_suite.json"
    
  - name: "run_accuracy_tests"
    type: "claude_batch"
    role: "accuracy_tester"
    batch_config:
      test_suite: "test_suite.json"
      metrics: ["exact_match", "f1_score", "bleu"]
      
  - name: "robustness_testing"
    type: "claude_robust"
    role: "robustness_tester"
    error_scenarios:
      - malformed_input
      - extreme_length
      - multilingual
      
  - name: "bias_analysis"
    type: "gemini"
    role: "bias_detector"
    gemini_functions:
      - name: "demographic_analysis"
      - name: "fairness_metrics"
```

#### 2.2 Performance Benchmarking Pipeline
**ID**: `model-evaluation-benchmark`  
**Purpose**: Benchmark model against standards  
**Complexity**: Medium  

**Benchmark Categories**:
- Speed and latency
- Token efficiency
- Cost analysis
- Quality metrics

#### 2.3 Regression Testing Pipeline
**ID**: `model-evaluation-regression`  
**Purpose**: Ensure model improvements don't degrade  
**Complexity**: Low  

**Features**:
- Historical comparison
- Performance tracking
- Automated alerts
- Trend analysis

### 3. Model Comparison Pipelines

#### 3.1 A/B Testing Pipeline
**ID**: `model-comparison-ab`  
**Purpose**: Compare models or prompts systematically  
**Complexity**: Medium  

**Workflow Structure**:
```yaml
steps:
  - name: "setup_experiment"
    type: "claude"
    role: "experiment_designer"
    prompt: "Design A/B test for comparing {model_a} vs {model_b}"
    
  - name: "parallel_execution"
    type: "parallel_claude"
    instances:
      - role: "model_a_executor"
        model_config: "{model_a_config}"
      - role: "model_b_executor"
        model_config: "{model_b_config}"
        
  - name: "statistical_analysis"
    type: "gemini_instructor"
    role: "statistician"
    output_schema:
      winner: "string"
      confidence: "float"
      p_value: "float"
      effect_size: "float"
```

#### 3.2 Multi-Model Ensemble Pipeline
**ID**: `model-comparison-ensemble`  
**Purpose**: Combine multiple models for better results  
**Complexity**: High  

**Ensemble Strategies**:
- Voting mechanisms
- Weighted averaging
- Stacking approaches
- Dynamic selection

#### 3.3 Cross-Provider Comparison
**ID**: `model-comparison-cross-provider`  
**Purpose**: Compare Claude vs Gemini for tasks  
**Complexity**: Medium  

**Comparison Metrics**:
- Quality of outputs
- Speed and latency
- Cost efficiency
- Feature capabilities

### 4. Fine-Tuning Pipelines

#### 4.1 Dataset Preparation Pipeline
**ID**: `fine-tuning-dataset-prep`  
**Purpose**: Prepare high-quality training datasets  
**Complexity**: High  

**Dataset Processing Steps**:
1. **Data Collection** (Claude)
   - Gather relevant examples
   - Ensure diversity
   - Balance categories

2. **Data Cleaning** (Reference: data-cleaning-standard)
   - Remove duplicates
   - Fix formatting
   - Validate quality

3. **Annotation** (Claude Session)
   - Add labels/tags
   - Generate explanations
   - Create metadata

4. **Augmentation** (Parallel Claude)
   - Generate variations
   - Add synthetic examples
   - Balance dataset

5. **Validation** (Gemini)
   - Check data quality
   - Verify distributions
   - Generate statistics

**Configuration Example**:
```yaml
steps:
  - name: "collect_examples"
    type: "claude_extract"
    role: "data_collector"
    extraction_config:
      source: "{data_sources}"
      criteria: "{selection_criteria}"
      format: "jsonl"
      
  - name: "annotate_data"
    type: "claude_session"
    role: "annotator"
    session_config:
      task: "Add training labels"
      batch_size: 100
      save_progress: true
      
  - name: "augment_dataset"
    type: "parallel_claude"
    instances: 5
    augmentation_strategies:
      - paraphrase
      - backtranslation
      - token_replacement
```

#### 4.2 Training Pipeline Orchestration
**ID**: `fine-tuning-orchestration`  
**Purpose**: Manage fine-tuning workflow  
**Complexity**: High  

**Workflow Management**:
- Dataset versioning
- Training job scheduling
- Hyperparameter tuning
- Model versioning

#### 4.3 Fine-Tuned Model Evaluation
**ID**: `fine-tuning-evaluation`  
**Purpose**: Evaluate fine-tuned model performance  
**Complexity**: Medium  

**Evaluation Focus**:
- Task-specific improvements
- Generalization testing
- Overfitting detection
- Comparison with base model

## Reusable Components

### Evaluation Metrics Components
```yaml
# components/steps/evaluation/metrics_calculator.yaml
component:
  id: "metrics-calculator"
  type: "step"
  
  supported_metrics:
    classification:
      - accuracy
      - precision
      - recall
      - f1_score
      - roc_auc
    generation:
      - bleu
      - rouge
      - bertscore
      - semantic_similarity
    custom:
      - task_specific_metric
```

### Prompt Templates Library
```yaml
# components/prompts/evaluation/test_case_generator.yaml
component:
  id: "test-case-generator"
  type: "prompt"
  
  template: |
    Generate {num_cases} test cases for {task_type}:
    
    Requirements:
    - Cover edge cases
    - Include normal cases
    - Test boundary conditions
    - Vary complexity
    
    Format each as:
    input: <test input>
    expected: <expected output>
    category: <edge|normal|boundary>
```

### Statistical Analysis Functions
```yaml
# components/functions/statistics.yaml
functions:
  - name: "perform_t_test"
    description: "Compare two model performances"
    parameters:
      model_a_scores: array
      model_b_scores: array
      confidence_level: number
      
  - name: "calculate_effect_size"
    description: "Measure practical significance"
    
  - name: "power_analysis"
    description: "Determine sample size needs"
```

## Performance Optimization

### 1. Caching Strategies
- Cache model outputs for reuse
- Store intermediate results
- Implement smart invalidation

### 2. Parallel Processing
- Distribute evaluation across instances
- Batch similar operations
- Load balance effectively

### 3. Resource Management
- Monitor token usage
- Optimize prompt lengths
- Implement rate limiting

## Quality Assurance

### 1. Validation Framework
```yaml
validation_rules:
  prompt_quality:
    - clarity_score: "> 0.8"
    - specificity: "high"
    - token_efficiency: "optimal"
    
  evaluation_validity:
    - sample_size: ">= 100"
    - statistical_power: ">= 0.8"
    - bias_checks: "passed"
```

### 2. Documentation Standards
- Document all prompts
- Track optimization history
- Maintain evaluation logs
- Version control datasets

## Integration Points

### 1. With Data Pipelines
- Use cleaned data for training
- Apply quality checks
- Leverage transformation tools

### 2. With Analysis Pipelines
- Feed results to analysis
- Generate insights
- Create visualizations

### 3. With DevOps Pipelines
- Deploy optimized models
- Monitor performance
- Automate retraining

## Best Practices

1. **Iterative Approach**: Start simple, refine gradually
2. **Systematic Testing**: Use consistent evaluation criteria
3. **Version Everything**: Prompts, datasets, results
4. **Statistical Rigor**: Ensure significant results
5. **Bias Awareness**: Always check for biases
6. **Cost Tracking**: Monitor resource usage

## Advanced Features

### 1. AutoML Integration
- Automated prompt optimization
- Hyperparameter search
- Architecture selection

### 2. Explainability Tools
- Prompt impact analysis
- Decision tracing
- Feature importance

### 3. Continuous Learning
- Online evaluation
- Drift detection
- Automated retraining

## Monitoring and Metrics

### 1. Pipeline Metrics
- Optimization cycles
- Improvement rates
- Resource efficiency
- Time to convergence

### 2. Model Metrics
- Performance trends
- Quality scores
- Consistency measures
- Cost per improvement

## Future Enhancements

1. **Visual Prompt Builder**: GUI for prompt construction
2. **AutoPrompt**: ML-driven prompt generation
3. **Model Zoo Integration**: Pre-trained model library
4. **Federated Evaluation**: Distributed testing
5. **Real-time Optimization**: Dynamic prompt adjustment