Most teams hit a wall when they need high‑quality labeled data: it’s too expensive to collect, privacy rules limit what you can use, or existing datasets are noisy and mislabeled. Synthetic data promises a way out, but only if it matches the real world. If you train on flawed synthetic data, you get flawed models, no matter how much you tune hyperparameters.
The core quality problem: distribution mismatch
The biggest risk is that your synthetic data looks good on paper but diverges sharply from the real distribution. A recent benchmark of synthetic text datasets showed that some models trained on high‑volume but low‑alignment data achieved 15‑25% lower accuracy on held‑out test sets compared to models trained on curated, domain‑mimicked data. The gap comes from distribution mismatch, not model capacity.
Quantitative metrics you can actually run
Start with two numbers: label accuracy and distribution overlap. For labeled data, run a small, human‑reviewed subset against the ground truth, aim for 98%+ label agreement on edge cases. For unstructured or multimodal data, compute overlap scores between synthetic and real distributions using KL‑divergence on feature embeddings or Earth Mover’s Distance on trajectories. A gap above 0.2 in KL‑divergence often flags serious misalignment that will hurt out‑of‑domain performance.
Domain checks you can’t automate away
Quantitative scores are necessary but not sufficient. Build a lightweight domain review pipeline that asks experts to spot check edge cases, assess realism, and flag logical inconsistencies. For text, test for factual hallucinations; for images, check lighting, occlusion, and object placement; for interaction trajectories, verify that user intents match the observed actions. This step catches systematic biases that pure metrics miss. Many teams report that domain reviews cut model failure rates by 30‑40% when they surface hidden edge cases early.
The takeaway: validate with a mix of quantitative metrics, distribution checks, and expert domain reviews before you commit a synthetic dataset to training.
How Coasty fits
Coasty specializes in synthetic data that captures realistic computer use behaviors. By running computer use agents on real desktops and browsers, Coasty generates interaction trajectories and datasets that mirror actual user workflows. This approach yields data that is both high‑volume and grounded in reality. Coasty’s service is custom and contact‑led: you work with the team to define requirements, and they deliver datasets tailored to your use case. There is no self‑serve product or fixed package, you request what you need and receive a solution designed around your constraints.
Don’t train on bad data. Validate your synthetic datasets with the right metrics and domain checks, then partner with a team that can deliver high‑fidelity data. Book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call to discuss your requirements and see how synthetic data can support your model development.
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