How to Measure Synthetic Data Quality Before You Train on It
Real-world data is scarce, noisy, and expensive to label. Synthetic data promises a way to fill gaps cheaply and quickly. But synthetic data can be garbage in, garbage out. If you train on flawed synthetic trajectories, your models will inherit those flaws. You waste compute and end up with unreliable agents. The key is to measure synthetic data quality before you commit to training.
Why synthetic data quality matters
Synthetic data is only as good as the process that generates it. A poorly designed simulation can create examples that look plausible but contain subtle biases or missing edge cases. Studies from the last couple of years show that model performance can drop 5 to 15 percent when synthetic data lacks diversity or contains hallucinations. The drop is worse for agents that need to navigate complex, multi-step workflows. That means you need a concrete, repeatable way to validate synthetic datasets before you feed them into training pipelines.
Concrete metrics you can use
Start with basic sanity checks, then move to more sophisticated validation. Consider these concrete metrics: - Coverage: Do you see at least 80 percent of the user tasks you care about? - Diversity: Does each task spawn multiple valid trajectories? - Consistency: Are similar inputs and steps producing similar outputs? - Completeness: Are all required fields filled correctly across the dataset? - Error rate: What percentage of steps are invalid or impossible? Run these checks on a sample of the synthetic data and compare them to your real data baselines. If synthetic coverage or diversity is significantly lower, the dataset may not be ready for training.
Human evaluation as a final checkpoint
Automated metrics are useful, but they can miss subtle quality issues. A human reviewer should sanity-check a subset of synthetic trajectories. Ask reviewers to evaluate: - Task success rate - Naturalness of interactions - Reasonableness of error handling - Adherence to interface rules A small human-in-the-loop sample of 20 to 50 trajectories can reveal problems that automated checks miss. If human reviewers flag more than 10 percent of the sample as problematic, you should refine your generation process before scaling up.
The one takeaway: validate synthetic data with a combination of automated metrics and human review before you train. This simple step can save you from model degradation and wasted compute.
How Coasty fits
Coasty focuses on synthetic data for computer use agents. We run real agents on real desktops and browsers to capture realistic interaction data. Our process can produce custom synthetic datasets and trajectories tailored to your workflows. The service is custom and contact-led, so we work with you to define the scope, quality criteria, and validation steps that make sense for your use case.
If you need high-quality synthetic data for training or evaluating AI agents, the first step is to talk to the team. Book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call to discuss your requirements and validation approach.