Synthetic Data — AI Glossary

Reference details

Related terms

• Fine-Tuning
• RLHF (Reinforcement Learning from Human Feedback)
• Supervised Fine-Tuning (SFT)

How it works

1. A generator model produces candidate examples, usually steered by a prompt that specifies the task, the distribution, and the difficulty wanted.
2. Candidates are filtered, and the filter does most of the work: verification against a checkable answer, agreement across samples, or a judge model.
3. Deduplication follows, because a generator left alone produces narrow, repetitive output that collapses the effective dataset size.
4. Diversity is enforced deliberately — seeding with varied topics, personas or constraints — since the generator's own distribution is narrower than the target.
5. Training on unfiltered output of a model similar to the one being trained degrades it, which is why the filter and the generator's relative strength are the whole design.

Example: Why the filter is the dataset

Generate 100,000 maths problems with worked solutions, then keep only those whose answer a verifier confirms. At a 60% correctness rate that leaves 60,000 verified examples and discards 40,000 — and the discarded ones are precisely the ones that would have taught wrong reasoning. Skip the filter and 40% of the training signal is incorrect. The generation is cheap and nearly worthless on its own; the verification is what converts it into data.

Commonly confused with

Model-generated training data. Distillation is the case where the generator is deliberately a stronger model and the goal is transferring its capability; augmentation is perturbing real examples rather than inventing new ones. Two constraints travel with synthetic data regardless of label: provider terms often restrict training on outputs, and generated data inherits the generator's blind spots, so it cannot teach what the generator does not know.

When to use it

Reach for it when:

• Real labelled data is scarce, expensive, or cannot be shared for privacy reasons.
• The task has a checkable answer, so generated examples can be verified rather than trusted.
• A stronger generator is available than the model being trained.

Reach for something else when:

• No filter or verifier exists, where unverified output trains in its own errors.
• The generator is the same strength as the target, which tends to narrow rather than extend capability.
• The real distribution has structure the generator has never seen, which synthetic data cannot invent.

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