Measuring Synthetic Data Quality Before You Train on It
Most teams hit the same wall: you need more labeled, realistic data, but real data is scarce, expensive, or full of privacy risk. Synthetic data looks like a free way out. But synthetic data can be noisy, biased, or just plain wrong. If you train on low‑quality synthetic data, you waste compute and ship broken models. Measuring quality before training is not optional. It’s a guardrail.
The baseline problem
Real-world data has hidden noise: labeling errors, edge cases, mislabeled clicks, and performance drift. Studies show that even 2, 5 percent mislabeled training examples can degrade model accuracy by several points in high‑stakes domains. Synthetic data is an attempt to fix this, but it introduces new noise: hallucinated actions, wrong intent labeling, or oversimplified user flows. Without a concrete measurement framework, you have no way to distinguish a synthetic dataset that improves your model from one that silently degrades it.
Use a test set you never touch during training
The simplest and most effective quality check is a hold‑out test set. This set must be real, labeled, and completely separate from what you feed your model during training. When you evaluate a model trained on synthetic data, you compare its performance on this hold‑out set against a model trained on real data. If synthetic data genuinely captures real patterns, the gap should be small. If the gap is large, synthetic data is missing something essential. In practice, many teams report that properly vetted synthetic datasets reduce the gap to under 3 percent in downstream tasks when the synthetic data is carefully generated.
Measure realism, not just coverage
Coverage tells you how many scenarios you’ve generated. Realism tells you how accurately those scenarios reflect real user behavior. You can measure realism by comparing distributional statistics between synthetic and real data: latency distributions, error rates, click-through trajectories, or intent labels. For example, if synthetic data shows a 30 percent click rate on a button that in real logs is only 12 percent, something is off. A common quality threshold is to keep distributional shifts under 15 percent for key metrics. Beyond aggregate stats, you can run a small human evaluation where annotators label synthetic interactions as “likely real” or “clearly synthetic.” If more than 10 percent are flagged, the dataset needs refinement.
Check for bias and spurious correlations
Synthetic data often over‑represents certain paths because they are easy to generate. This creates bias that hurts model generalization. To catch this, audit your synthetic data for spurious correlations: for example, a relationship between a synthetic button location and a particular task outcome that doesn’t exist in real usage. You can quantify this by checking whether feature distributions from synthetic data cluster tightly around real data across multiple dimensions. If synthetic examples cluster in high‑density artificial regions, you’re likely over‑fitting to generation heuristics. The fix is to inject more diversity and then re‑measure the distribution gap.
Quality is a process, not a one-time check. Set up automated baselines on a real hold‑out set, compare distributional statistics, and re‑audit for bias after each generation cycle.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This lets teams generate synthetic datasets that mirror how people actually work, including complex multi-step workflows and realistic error handling. The service is fully custom and contact‑led: you work with the Coasty data team to define your requirements, scope, and quality targets, then they produce a tailored dataset. No generic SKUs, no off‑the‑shelf pricing, just a bespoke solution matched to your model and evaluation needs.
Don’t ship a model trained on synthetic data you haven’t measured. Start by setting up a real hold‑out test set and measuring distributional gaps and bias. If you need synthetic data that’s genuinely realistic for computer use tasks, book a data call with the Coasty team at https://cal.com/coasty/coasty-data-call .