Measuring Synthetic Data Quality Before You Train on It
Most teams hit a wall: either they lack enough labeled examples or real-world data is risky to use. Synthetic data offers a way out, but only if it actually matches reality. Bad synthetic data trains bad models. The problem is knowing when the data is good enough.
The risk of synthetic garbage
A 2023 study by researchers at Stanford and OpenAI showed that models trained on low-quality synthetic data can degrade performance by up to 30 percent compared to real data. This happens when synthetic samples introduce systematic errors or unrealistic distributions. For example, synthetic emails might have perfect grammar but wrong sender domains, or synthetic UI screenshots might miss critical affordances like buttons or dropdowns. These subtle flaws compound during fine-tuning, leading to models that hallucinate or ignore edge cases.
Quantitative quality checks you should run
- ●Distribution similarity: Use KL divergence or Wasserstein distance to compare feature distributions (e.g., token frequencies, bounding box sizes, or intent labels). A value above 0.3 often indicates a mismatch.
- ●Label agreement between models: Run a strong baseline model on your synthetic dataset. If it achieves <80 percent agreement with ground truth, the data is noisy.
- ●Human-in-the-loop spot checks: Randomly sample 200, 500 items and have a domain expert label them. Target >90 percent accuracy for the synthetic set to be considered reliable.
- ●Adversarial robustness: Feed synthetic examples through an adversarial attack and measure success rate. If attacks frequently fool your synthetic samples, the data lacks nuance.
- ●Temporal freshness: Verify that synthetic examples span realistic time ranges and reflect current patterns (e.g., recent UI changes or product updates).
Think of synthetic data quality like a checklist: distribution checks, model agreement, human validation, adversarial stress tests, and temporal relevance. Run all of them before you commit to training.
How Coasty fits
Coasty operates computer use agents on real desktops and browsers. These agents interact with actual applications and capture realistic interaction data, including clicks, navigation paths, and UI states. This approach lets Coasty generate synthetic datasets that reflect real-world complexity and edge cases. Coasty offers a custom synthetic data service tailored to your specific use case. This is a contact-led service with no fixed packages or public pricing, you start by discussing your requirements with the team.
Practical workflow for synthetic data projects
- ●Define ground truth: Decide which aspects of reality matter for your task (e.g., button states, error types, user intent).
- ●Generate synthetic samples using Coasty agents: Ask for a pilot batch that covers key scenarios.
- ●Run quality checks: Apply the quantitative and qualitative checks described above.
- ●Iterate: Use feedback to refine prompts, agent configurations, or sampling strategies before scaling.
- ●Monitor during training: Watch for degradation in validation metrics and re-audit synthetic samples if needed.
Don't train on synthetic data without proof. Start by measuring distribution alignment, model agreement, and human accuracy. If you need high-quality synthetic datasets that reflect real desktop and browser interactions, book a data call with the Coasty team at https://cal.com/coasty/coasty-data-call .