Synthetic data for testing AI systems: when it helps and when it misleads
Generated test cases can cover rare scenarios and bootstrap evals, but they can also mirror the generator's blind spots. A short checklist for using them well.
Synthetic data means test material produced by a model or a script instead of collected from real use: invented customer messages, fabricated documents, simulated conversations. Teams reach for it when real examples are scarce. It is a useful tool for testing an AI system, and a misleading one when it is treated as a substitute for reality.
Where it helps
Bootstrapping. Practitioner guidance says synthetic data lets you begin error analysis before you have enough production traffic [3].
Rare failures. It can test a known failure that seldom appears in real data [3].
Coverage. Defining the dimensions along which users vary, then combining them, reaches edge cases that a hand-written set tends to miss [3].
Safe fixtures. Invented records mean test environments need not contain real customer data. This is a technical hygiene benefit.
Where it misleads
The same guidance is direct about limits: synthetic data cannot show how common a failure is in production, and it may miss details that matter in specialized domains [3].
Two further risks follow from how generation works. These are our reasoning, not findings from the sources:
A generator writes what it finds typical, so a test set can inherit its blind spots. The cases your system fails on may be exactly the ones the generator never produces.
Scoring a model on text it generated, or grading with a model from the same family, can flatter it.
Training is a separate matter. A 2024 Nature paper found that training indiscriminately on model-generated content "causes irreversible defects in the resulting models, in which tails of the original content distribution disappear", and noted that data about genuine human interactions becomes more valuable as generated content spreads [1]. That result concerns training, not testing, but it is a reminder that rare real cases are what generated data loses first.
NVIDIA's open Nemotron-4 340B pipeline shows how vendors manage the quality problem: one model generates data, and a separate reward model filters for high-quality responses, grading helpfulness, correctness, coherence, complexity and verbosity [2]. Generation is paired with an independent check.
Use | Helps | Misleads |
|---|---|---|
Rare or edge cases | Reaches them on demand [3] | Cases the generator cannot imagine stay untested |
Early evals | Starts error analysis without traffic [3] | Says nothing about real failure frequency [3] |
Fixtures | No real data needed | Too tidy compared with real inputs |
Scoring | Cheap, repeatable | Self-grading can flatter the model |
A short checklist
Seed from real examples where you have them, and describe real failure modes before generating.
Keep a human-labelled holdout that no generator touched.
Vary the generator and the grader, not just the prompt.
Track provenance: mark every item as real or synthetic and record which model made it.
Compare results on synthetic and real data once real data arrives [3].
Sources
- AI models collapse when trained on recursively generated data Nature (Shumailov, Shumaylov, Zhao, Papernot, Anderson, Gal), 2024-07-24. www.nature.com/articles/s41586-024-07566-y
- NVIDIA Releases Open Synthetic Data Generation Pipeline for Training Large Language Models NVIDIA Blog, 2024-06-14. blogs.nvidia.com/blog/nemotron-4-synthetic-data-generation-l
- Q: What is the best approach for generating synthetic data? Hamel Husain and Shreya Shankar (evals FAQ), 2025-06-01. hamel.dev/blog/posts/evals-faq/what-is-the-best-approach-for
