Synthetic Data
Synthetic data is training or eval data generated by a model rather than collected from the world — filling gaps, balancing classes, bootstrapping fine-tunes.
Synthetic data is data produced by a model instead of gathered from users or the world — generated examples used to train, fine-tune, or evaluate AI systems.
It solves the data bottleneck that dominates applied ML: real examples are scarce, expensive to label, privacy-encumbered, or missing exactly the edge cases you need. A strong LLM can manufacture variations from seeds, label at scale, simulate rare scenarios, and — in the distillation pattern — generate the entire training set for a smaller model. Modern fine-tuning pipelines are substantially synthetic, and frontier labs' post-training famously leans on model-generated data.
The craft is quality control, because generation is cheap and good data isn't: synthetic distributions run smoother than reality, teacher mistakes propagate, and naive recycling degrades models. Production practice filters aggressively, validates against real held-out data, and never lets the eval set be synthetic-only. The applied versions live in Preparing a Fine-Tuning Dataset and the finetune-dataset-builder skill — and on the eval side, synthesizing the edge cases your logs lack is a standard move in Write Evals for an LLM App.
Frequently asked questions
- Is it safe to train on synthetic data?
- With curation, yes — it powers most modern fine-tunes and a large share of frontier post-training. The risks are real but manageable: distribution narrowing (generated data is smoother than reality), inherited teacher errors, and degenerate feedback loops if you recycle outputs blindly. The mitigation is always the same — filter hard, verify samples, and keep real data in the mix, especially for evaluation.
- Where does synthetic data help most in LLM work?
- Three places: fine-tuning datasets (generate variations from seed examples, or distill a teacher model), eval sets (synthesize edge cases your logs don't cover yet), and privacy (shareable stand-ins for sensitive records). The discipline is identical everywhere: generation is cheap, curation is the work.
Related
- Preparing a Fine-Tuning Dataset: Cleaning, Synthetic Data, and Eval SplitsThe dataset is the model. How to build a fine-tuning dataset that works — format, curation, cleaning, synthetic augmentation, and a leak-free eval split.
- DistillationDistillation trains a smaller model to imitate a larger one — using its outputs as training data to get most of the capability at a fraction of the cost.
- Fine-TuningFine-tuning continues training a pretrained model on your own examples, changing its weights to teach durable behavior, format, or domain style.
- Write Evals for an LLM App: From Zero to a CI GateHow to evaluate an LLM feature — build a dataset, choose metrics, set a baseline, score offline, add an LLM judge, and gate CI so quality changes are measured.
- Finetune Dataset BuilderTurn raw examples into a training-ready fine-tuning dataset — normalize to the trainer's chat/instruction format, deduplicate (including near-duplicates), strip PII, balance, validate the schema and token lengths, and carve a leak-free eval split. Use when you have raw examples and need a clean, formatted, split dataset before training.
- Batch InferenceBatch inference processes many LLM requests asynchronously instead of one-at-a-time interactively — typically at ~50% discount via provider batch APIs.
- Eval DatasetAn eval dataset is the curated set of test cases — inputs with expected outcomes — that an LLM application's quality is measured against.