Fine-tuning Strategy

Overview

The Fine-tuning Strategy document outlines the approach, methodology, and infrastructure for fine-tuning Large Language Models (LLMs) to optimize their performance for specific engineering roles and tasks within the Engineering AI Agent system.

System Architecture

The fine-tuning system follows a structured pipeline for collecting data, training models, evaluating results, and deploying optimized models:

Key Components

1. Data Collection and Processing Pipeline

The data pipeline handles the collection, cleaning, transformation, and validation of fine-tuning datasets:

class DataPipeline:
    """Pipeline for processing fine-tuning data"""
    
    def __init__(self, config):
        """Initialize pipeline with configuration"""
        self.config = config
        self.filters = self._initialize_filters(config.get('filters', []))
        self.transformers = self._initialize_transformers(config.get('transformers', []))
        self.validators = self._initialize_validators(config.get('validators', []))
    
    def process(self, raw_data):
        """Process raw data through the pipeline"""
        filtered_data = self._apply_filters(raw_data)
        transformed_data = self._apply_transformers(filtered_data)
        validated_data = self._apply_validators(transformed_data)
        return validated_data
    
    def _apply_filters(self, raw_data):
        """Apply filters to the raw data"""
        filtered_data = raw_data
        for filter_fn in self.filters:
            filtered_data = filter_fn(filtered_data)
        return filtered_data
    
    def _apply_transformers(self, filtered_data):
        """Apply transformations to the filtered data"""
        transformed_data = filtered_data
        for transformer in self.transformers:
            transformed_data = transformer(transformed_data)
        return transformed_data
    
    def _apply_validators(self, transformed_data):
        """Apply validation to the transformed data"""
        for validator in self.validators:
            validation_result = validator(transformed_data)
            if not validation_result['valid']:
                raise ValidationError(validation_result['message'])
        return transformed_data
    
    def _initialize_filters(self, filter_configs):
        """Initialize filter functions from configuration"""
        # Implementation details...
        return filters
    
    def _initialize_transformers(self, transformer_configs):
        """Initialize transformer functions from configuration"""
        # Implementation details...
        return transformers
    
    def _initialize_validators(self, validator_configs):
        """Initialize validator functions from configuration"""
        # Implementation details...
        return validators

Data Format

Fine-tuning data follows a standardized format:

{
  "examples": [
    {
      "id": "example-001",
      "role": "software-architect",
      "task_type": "architecture-review",
      "messages": [
        {
          "role": "system",
          "content": "You are an experienced software architect helping review and improve system designs."
        },
        {
          "role": "user",
          "content": "Please review this microservice architecture diagram and identify any potential issues with scalability or resilience: [Architecture diagram with 3 microservices connected via REST APIs and a shared database]"
        },
        {
          "role": "assistant",
          "content": "I've analyzed the architecture diagram and identified several potential issues:\n\n1. **Shared Database Anti-pattern**\n   The microservices are sharing a single database, which creates tight coupling and limits independent scalability. Each service should ideally have its own data store.\n\n2. **Synchronous Communication**\n   The exclusive use of REST APIs for inter-service communication could create availability issues. Consider using asynchronous communication patterns with message queues for non-blocking operations.\n\n3. **Single Points of Failure**\n   I don't see any redundancy in the database or service deployment. Consider implementing database replication and deploying multiple instances of each service.\n\n4. **Missing API Gateway**\n   There's no API gateway to handle cross-cutting concerns like authentication, routing, and rate limiting.\n\nRecommended improvements:\n1. Implement database-per-service pattern with eventual consistency\n2. Add message broker for asynchronous communication\n3. Implement service discovery mechanism\n4. Add API gateway for client communication\n5. Consider CQRS pattern for complex data operations"
        }
      ],
      "metadata": {
        "source": "expert_annotation",
        "difficulty": "intermediate",
        "quality_score": 0.95,
        "tags": ["architecture", "microservices", "scalability"]
      }
    }
  ]
}

2. Training Pipeline

The training pipeline orchestrates the fine-tuning process across different infrastructure components:

Hyperparameter Configuration

Training hyperparameters are defined in YAML configuration:

fine_tuning:
  base_model: gpt-4-0125-preview
  training:
    epochs: 3
    batch_size: 4
    learning_rate: 2.0e-5
    weight_decay: 0.01
    warmup_steps: 500
    lr_scheduler: cosine
    gradient_accumulation_steps: 8
  validation:
    split: 0.1
    metrics:
      - accuracy
      - perplexity
      - exact_match
      - rouge_l
      - custom_code_quality
  early_stopping:
    patience: 3
    min_delta: 0.01
    monitor: val_loss

3. Evaluation Framework

The evaluation framework provides comprehensive assessment of fine-tuned models:

def evaluate_model(model_id, evaluation_datasets, metrics):
    """
    Evaluate a fine-tuned model against multiple datasets using specified metrics
    
    Args:
        model_id: The ID of the model to evaluate
        evaluation_datasets: List of datasets to evaluate against
        metrics: List of metrics to calculate
        
    Returns:
        Dictionary containing evaluation results
    """
    results = {}
    
    for dataset in evaluation_datasets:
        dataset_results = {}
        
        # Load dataset
        eval_data = load_dataset(dataset['path'])
        
        # Run inference
        predictions = run_model_inference(model_id, eval_data)
        
        # Calculate metrics
        for metric_name in metrics:
            metric_fn = get_metric_function(metric_name)
            score = metric_fn(predictions, eval_data.references)
            dataset_results[metric_name] = score
            
        results[dataset['name']] = dataset_results
    
    # Calculate aggregate scores
    results['aggregate'] = calculate_aggregate_scores(results)
    
    return results

Evaluation Metrics

Models are evaluated using multiple dimensions:

1. Task-specific Performance: Accuracy on engineering tasks
2. Code Quality: Assessment of generated code quality
3. Technical Correctness: Factual accuracy of responses
4. Response Efficiency: Token economy and relevance

4. Model Versioning

Models follow a structured versioning scheme:

ea-[role]-[base_model]-[version]-[timestamp]

For example:

• ea-software-developer-gpt4-v2.3-20250215
• ea-system-architect-llama3-v1.5-20250301

5. Deployment Workflow

Model deployment follows a staged approach:

1. Canary Testing: Initial deployment to 5% of traffic
2. Validation: Automated checks against performance criteria
3. Staged Rollout: Incremental traffic increase with monitoring
4. Gradual Rollout: Increasing traffic percentage over time
5. Full Deployment: Complete replacement of previous model

Kubernetes Deployment Configuration

apiVersion: apps/v1
kind: Deployment
metadata:
  name: model-ea-software-developer-v2
  namespace: ai-services
spec:
  replicas: 3
  selector:
    matchLabels:
      app: model-server
      model: ea-software-developer
      version: v2
  template:
    metadata:
      labels:
        app: model-server
        model: ea-software-developer
        version: v2
    spec:
      containers:
      - name: model-server
        image: ea-registry.io/model-server:1.4.2
        env:
        - name: MODEL_PATH
          value: /models/ea-software-developer-gpt4-v2.3-20250215
        - name: ENABLE_TRACING
          value: "true"
        volumeMounts:
        - name: model-volume
          mountPath: /models
        resources:
          limits:
            cpu: "4"
            memory: "16Gi"
            nvidia.com/gpu: "1"
          requests:
            cpu: "2"
            memory: "8Gi"
        readinessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 30
          periodSeconds: 10
      volumes:
      - name: model-volume
        persistentVolumeClaim:
          claimName: model-storage-pvc

Role-specific Fine-tuning

Different engineering roles require specialized fine-tuning focus areas:

Role	Focus Areas	Example Tasks
Software Developer	Code generation, debugging, refactoring	Implement algorithm, fix bug, improve code quality
Software Architect	System design, architecture patterns, component interaction	Design microservice architecture, evaluate tech stack, plan migration
QA Engineer	Test planning, test case generation, bug identification	Write test plan, generate test cases, analyze logs
DevOps Engineer	Infrastructure definition, CI/CD pipeline optimization, operational stability	Create Kubernetes manifest, optimize CI pipeline, debug production issue
Product Manager	Requirements analysis, feature prioritization, specification writing	Parse user story, rank features, write acceptance criteria

Continuous Improvement Cycle

Fine-tuning follows a continuous improvement cycle:

Feedback Collection

Feedback is collected from multiple sources:

1. User Ratings: Explicit ratings and feedback on model outputs
2. Expert Reviews: Domain expert assessments of model performance
3. Performance Metrics: Automated measurement of success rates
4. Error Analysis: Detailed review of failure cases

API Endpoints

Endpoint	Method	Description
/api/v1/fine-tuning/jobs	POST	Create a new fine-tuning job
/api/v1/fine-tuning/jobs/{job_id}	GET	Get status of a fine-tuning job
/api/v1/fine-tuning/models	GET	List available fine-tuned models
/api/v1/fine-tuning/models/{model_id}	GET	Get details of a specific model
/api/v1/fine-tuning/evaluations	POST	Create a new evaluation job
/api/v1/fine-tuning/feedback	POST	Submit feedback for model improvement

Future Enhancements

1. Few-shot Learning: Implementing dynamic few-shot example selection
2. Cross-role Knowledge: Enabling knowledge sharing across specialized models
3. Parameter-efficient Fine-tuning: Implementing LoRA and QLoRA techniques
4. Human-in-the-loop: Adding expert validation and correction workflows
5. Multi-modal Fine-tuning: Extending to code+text+diagram understanding