How to Measure Synthetic Data Quality Before You Train on It
You do not have enough labeled data, or real data is too sensitive, expensive, or hard to obtain. You turn to synthetic data. But synthetic data behaves like a black box. Feed it a model, and you might see a dip in performance. The warning signs are subtle: model hallucinates, fails edge cases, or overfits to obvious artifacts. The problem is not the model. The problem is the data. Poor quality synthetic data corrupts training, wastes compute, and hides real bugs. You need to measure quality before you commit to training.
The quality gap between synthetic and real data
Studies show that up to 30 percent of synthetic datasets contain subtle distribution shifts compared with real-world data. These shifts show up as lower accuracy, higher error rates on out-of-distribution samples, or drift in downstream tasks. A 2023 evaluation of synthetic code generation datasets found that 15 percent of examples had invalid syntax despite passing heuristic checks. Another study on synthetic dialogue datasets revealed that 20 percent of responses were factually incorrect or contradicted the premise. Synthetic data is not automatically good data. It can be close, but it can also be off in ways that are hard to spot without the right tools.
Concrete metrics that catch real problems
- ●Distribution alignment: Use KL divergence or Wasserstein distance between synthetic and real feature distributions. Aim for changes under 0.1 on normalized embeddings.
- ●Label fidelity: For classification, run a small held-out real dataset through a high-confidence model and compare predicted labels with ground truth. High agreement indicates strong label quality.
- ●Semantic correctness: Use a separate model to verify that generated responses match the original prompt or task constraints. Track false positives and false negatives.
- ●Edge case coverage: Audit synthetic data for rare but important scenarios. If your domain has rare failure modes, synthetic generation should explicitly generate them.
- ●Error propagation test: Run a subset of synthetic examples through your training pipeline, monitor gradient norms, and check for divergence or exploding losses.
Quality checks are cheap compared to retraining. Invest time in data diagnostics before you pay for large-scale training runs.
A practical checklist for synthetic data validation
- ●Sanity checks: Validate that generated outputs are syntactically valid, within expected ranges, and do not contain prohibited keywords or patterns.
- ●Human review: Have subject-matter experts sample synthetic examples and rate them for realism and correctness. Aim for at least 5 percent of the dataset in the first pass.
- ●Benchmark comparison: Run a small model on both real and synthetic test sets. If synthetic performance is more than 5 percent lower, investigate the discrepancy.
- ●A/B test: Reserve a small portion of real data and compare model performance when trained on real vs synthetic data. Look for systematic gaps.
- ●Longitudinal drift: If you generate data over time, check for changes in distribution. Distribution drift can indicate generation instability.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This approach produces synthetic datasets and trajectories that reflect how humans actually interact with software. The team can produce custom synthetic data tailored to your domain, tools, and workflows. This is a custom service, not a packaged product. You talk directly with the Coasty data team to define the scope, collect real interaction traces where needed, and generate synthetic datasets that match your requirements.
Don't train on synthetic data you haven't validated. Start with the metrics and checks above to catch distribution shifts and label errors early. If you want a custom synthetic dataset that is grounded in real interaction data, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call.