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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