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Choosing Between Synthetic and Real Dataset Testing

Default Recommendation: Use Real Data Whenever Possible​

Real production data provides the most accurate performance predictions because:

  • Input token distribution matches actual user behavior
  • Query complexity reflects real use cases
  • Performance results directly correlate to production experience
  • Identifies issues with specific prompt patterns that synthetic data might miss

When to Use Synthetic Data (Scenarios 1-4):​

  • Initial deployment validation before production data exists
  • Standardized comparisons across different systems (apples-to-apples)
  • Testing extreme edge cases (very long prompts, burst patterns)
  • Quick CI/CD validation where consistency matters more than realism
  • Public benchmarking where real data cannot be shared

Best Practice: Continuous Dataset Validation​

Production workloads evolve over time. To ensure your benchmarks remain representative:

  1. Capture anonymized production prompts periodically for benchmark datasets
  2. Monitor distribution drift between test data and production traffic:
   # Compare token length distributions
   # Production: median=450, p95=1200
   # Test data: median=512, p95=2048
   # → Test data may overestimate TTFT
  1. Refresh test datasets quarterly to match current production patterns
  2. Version your datasets to track performance changes over time

This mirrors traditional ML continuous evaluation practices applied to inference performance testing.