Most teams struggle to get enough labeled data, and real-world data can be noisy, biased, or expensive to acquire. Synthetic data offers a scalable alternative, but it introduces a new risk: garbage in, garbage out. If your synthetic data doesn't reflect real user behavior or task requirements, your model will underperform or fail in production.
The quality gap in synthetic data
A 2023 study from the Allen Institute for AI found that models trained on synthetic instruction-following data sometimes performed 15 to 30 percent worse on real human evaluations than those trained on verified human data. The failure modes were specific: hallucinations, incorrect tool usage, and off-by-one errors in multi-step workflows. These problems don't disappear just because the data is artificially generated.
Start with a validation set, not the whole dataset
Treat synthetic data like any other dataset: hold out a validation sample before you train. Use human annotators to label a subset of the synthetic trajectories and compare them to real labels. This gives you a concrete quality score you can track over time. If the synthetic set has a label agreement rate below your threshold, you have a problem before you even start training.
Check coverage and edge cases
Synthetic generators often shine on the happy path and miss edge cases. Run a coverage analysis to see which user actions, error states, or rare scenarios appear in your synthetic dataset. For example, if your real-world logs show that 3% of users restart a workflow after a failed payment, but your synthetic set contains no restarts, you are missing a critical failure mode. Tools like confusion matrices and precision-recall curves can surface these gaps.
Measure realism with behavioral metrics
Synthetic data should mimic real user behavior. Look at metrics like mouse velocity, click order, and hover duration. A 2024 analysis of browser automation data found that synthetic trajectories could match real behavior within 8% on average, but some edge cases deviated by 40% due to oversimplified automation rules. Compare synthetic interaction logs against anonymized real logs using statistical tests such as KS or Mann-Whitney U to quantify realism.
Quality isn't binary. It's a spectrum. Measure coverage, realism, and label agreement early, and iterate on your generation pipeline before you commit to training.
How Coasty fits
Coasty runs computer-use agents on real desktops and browsers to capture realistic interaction data and trajectories. This approach helps produce synthetic datasets that mirror actual user workflows, which you can then evaluate and refine. Coasty's synthetic data offering is custom and contact-led, so you work directly with the team to define requirements, design datasets, and validate quality before scale.
Don't guess at synthetic data quality. Validate it with real test cases, behavioral metrics, and human review. Ready to explore how Coasty can help you build high-quality synthetic data for your use case? Book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call.
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