Synthetic Data
Guidelines and best practices for generating and using synthetic data effectively
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.
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
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
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
Iterative Development Process
Synthetic data generation works best as an iterative process. Here’s how to approach it:
Initial Testing Phase
-
Model Selection
- Generate small batches with different base models
- Compare quality across models
- Select the most promising model for further testing
-
Parameter Tuning
- Start with small sample sizes
- Test different generation strategies
- Adjust parameters based on results
Refinement Loop
-
Quality Review
- Manually review generated samples
- Identify common issues or patterns
- Document successful and unsuccessful examples
-
Dataset Improvement
- Remove or fix poor examples
- Refine seed dataset based on findings
- Add examples to address gaps
-
Regeneration
- Generate new samples with improved seed data
- Compare results with previous iterations
- Continue refining until quality goals are met
Validation Strategy
Implement a robust validation process:
-
Quality Metrics
- Compare synthetic data statistics with real data
- Check for unwanted patterns or biases
- Verify diversity of generated examples
-
Use Case Testing
- Test synthetic data in your intended application
- Measure performance against objectives
- Compare results with real data performance
-
Continuous Monitoring
- Track quality over multiple generations
- Monitor for degradation or drift
- Adjust process based on findings
Cost Optimization
Optimize your synthetic data generation costs:
-
Model Selection
- Start with cost-effective models
- Use more expensive models only when needed
- Balance quality vs. cost
-
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
- Use
Common Pitfalls to Avoid
-
Dataset Issues
- Too few seed examples
- Inconsistent formatting
- Poor quality examples
- Biased or unbalanced selection
-
Quality Control
- Not reviewing generated samples
- Missing validation steps
- Ignoring error patterns
- Not documenting issues
Next Steps
After implementing these best practices: