Synthetic Data

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Creating a High-Quality Seed Dataset

The foundation of effective synthetic data generation is a well-prepared seed dataset. Follow these guidelines to ensure your seed data leads to high-quality synthetic examples.

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Representative Sample Selection

When selecting or creating your seed examples:

• Initial Selection: Hand-pick examples from existing datasets or carefully craft new ones
• Diversity: Include at least one example of each variation or pattern you want the model to learn
• Balance: Avoid over-representation of any particular class or feature
• Minimum Size: Start with at least 10 high-quality examples

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Data Quality Standards

Maintain high quality standards across your seed dataset:

• Accuracy: Verify that all examples are correct and error-free
• Consistency: Use uniform formatting and structure across all examples
• Completeness: Ensure each example contains all necessary information

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Example Structure

Format individual examples effectively:

• Overview: Include a 1-2 line description at the top of each example explaining the task
• Zero-Shot Format: Keep examples self-contained without demonstrations or multi-step instructions
• Clear Boundaries: Maintain clear separation between prompt and completion
• Consistent Style: Use consistent writing style and terminology

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Iterative Development Process

Synthetic data generation works best as an iterative process. Here’s how to approach it:

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Initial Testing Phase

1. Model Selection
   • Generate small batches with different base models
   • Compare quality across models
   • Select the most promising model for further testing
2. Parameter Tuning
   • Start with small sample sizes
   • Test different generation strategies
   • Adjust parameters based on results

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Refinement Loop

1. Quality Review
   • Manually review generated samples
   • Identify common issues or patterns
   • Document successful and unsuccessful examples
2. Dataset Improvement
   • Remove or fix poor examples
   • Refine seed dataset based on findings
   • Add examples to address gaps
3. Regeneration
   • Generate new samples with improved seed data
   • Compare results with previous iterations
   • Continue refining until quality goals are met

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Validation Strategy

Implement a robust validation process:

1. Quality Metrics
   • Compare synthetic data statistics with real data
   • Check for unwanted patterns or biases
   • Verify diversity of generated examples
2. Use Case Testing
   • Test synthetic data in your intended application
   • Measure performance against objectives
   • Compare results with real data performance
3. Continuous Monitoring
   • Track quality over multiple generations
   • Monitor for degradation or drift
   • Adjust process based on findings

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Cost Optimization

Optimize your synthetic data generation costs:

1. Model Selection
   • Start with cost-effective models
   • Use more expensive models only when needed
   • Balance quality vs. cost
2. Strategy Selection
   • Use single_pass for initial testing
   • Reserve mixture_of_agents for when higher quality is needed
   • Consider cost-benefit ratio for your use case

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Common Pitfalls to Avoid

1. Dataset Issues
   • Too few seed examples
   • Inconsistent formatting
   • Poor quality examples
   • Biased or unbalanced selection
2. Quality Control
   • Not reviewing generated samples
   • Missing validation steps
   • Ignoring error patterns
   • Not documenting issues

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Next Steps

After implementing these best practices:

1. Generate synthetic data
2. Prepare for fine-tuning
3. Start the fine-tuning process
4. Evaluate your results