Synthetic data is appealing because it promises both scale and safety: more examples, fewer privacy issues, and faster experimentation. Used well, it can unlock evaluation and testing in data-sensitive environments. Used poorly, it creates false confidence and brittle systems.
Where synthetic data helps
- Privacy-preserving sharing. Create non-identifying datasets for development and vendor evaluations when real data cannot leave secure zones.
- Rare-event coverage. Augment edge cases (fraud patterns, unusual support tickets, safety scenarios) that are under-represented in real logs.
- Scenario testing. Generate structured test suites for workflows so changes can be regression-tested quickly.
- Adversarial evaluation. Create attack-style inputs for red teaming and safety testing (see LLM red teaming).
Where synthetic data hurts
The biggest risks are subtle:
- Distribution shift. Synthetic examples often reflect what a model thinks “typical” looks like, not what users actually do.
- Evaluation contamination. If you generate evaluation data with the same model family you are testing, you can inflate scores without improving real-world performance.
- Hidden correlations. Synthetic generators introduce artefacts (phrasing, formats, label shortcuts) that downstream models learn.
- False safety signals. Synthetic data can be “cleaner” than reality, masking the messy inputs that cause incidents.
Practical principles
Teams that succeed with synthetic data apply a few disciplines:
- Track provenance. Label synthetic vs real, record the generator, prompts, constraints and parameters.
- Keep a real holdout. Always validate changes against a protected, real-world evaluation set.
- Optimise for tasks, not realism. Synthetic data is most useful when it targets a specific failure mode or coverage gap.
- Use governance and contracts. Treat synthetic datasets as governed assets with owners and documentation (see data contracts).
Synthetic data is best understood as a capability inside your evaluation and delivery system—not a shortcut around it. The goal is to learn faster while staying honest about what you do (and do not) know.