Synthetic data — training data generated by AI models rather than collected from real-world sources — has become one of the most important techniques in the ML practitioner’s toolkit. When you don’t have enough real data, or when real data is too expensive, sensitive, or biased to use directly, you generate synthetic examples to fill the gap.

The technique works surprisingly well in certain scenarios and fails predictably in others. Understanding when to use it is becoming a critical skill.

How It Works

The basic approach: use a capable language model to generate training examples following a desired pattern. For classification tasks, generate text examples for each category. For instruction-following, generate prompt-response pairs. For structured extraction, generate documents with known entities.

The process involves defining your schema, creating 10-50 high-quality seed examples by hand, generating at scale with a language model, filtering and validating the output, then training your target model on the result.

The Stanford Alpaca project was an early demonstration — using GPT-3.5 to generate 52,000 instruction-following examples that fine-tuned LLaMA to match larger models on certain benchmarks.

Where Synthetic Data Works Well

Classification and Categorisation

Highly effective because categories can be described precisely and language models are good at generating text that fits descriptions. Teams regularly reduce labelling costs by 70-80% using a mix of real data (20-30%) and synthetic data (70-80%). The real data grounds the model; synthetic data provides volume and edge case coverage.

Structured Extraction

Named entity recognition and information extraction benefit enormously because you know the ground truth by construction. When you generate a synthetic invoice, you know exactly what the line items and totals are. The challenge is ensuring synthetic documents include the formatting variations, typos, and inconsistencies that real documents contain.

Balancing Underrepresented Classes

Imbalanced datasets are common — 10,000 examples of common cases, 50 of rare ones. Synthetic generation can balance the dataset. One manufacturing inspection project used synthetic descriptions of rare defects to dramatically improve detection rates where real examples were scarce.

Where Synthetic Data Falls Short

Capturing Real-World Distribution

Synthetic data reflects the generating model’s understanding, not actual data distribution. If real customer messages have specific patterns — regional idioms, domain jargon — the generating model might not capture them. Models trained on synthetic data can perform well on benchmarks but poorly on actual inputs.

Domain-Specific Knowledge

For specialised domains — medical records, legal documents — generic models produce data that looks correct but contains factual errors. One firm we talked to about their medical AI deployment shared that synthetic clinical documents used correct terminology but combined conditions and medications in ways clinicians immediately flagged as unrealistic. They used synthetic data only for augmentation rather than as a primary source.

Tasks Requiring Real-World Grounding

Recommendation systems, search ranking, and anything depending on actual user behaviour can’t rely on synthetic data alone. Similarly, time-series forecasting and sensor data need real measurements governed by physics, not language patterns.

Practical Guidelines

Start with real data, augment with synthetic. Even 100-200 real examples combined with synthetic augmentation outperforms synthetic-only datasets of 10x the size.

Validate relentlessly. Check internal consistency, factual plausibility, and diversity. The Hugging Face synthetic data guides cover practical validation approaches.

Use stronger models to generate, weaker to train. Frontier models provide quality; smaller models provide cost-effective inference.

Iterate on generation prompts. Vague prompts produce generic data. Detailed prompts with explicit constraints produce useful data.

Measure the contribution. Compare performance on real-only, synthetic-only, and blended datasets. If synthetic data isn’t improving beyond the real-data baseline, you’re adding noise.

The Bottom Line

Synthetic data is powerful for specific problems — data scarcity, class imbalance, privacy constraints. It’s not a replacement for understanding your real data distribution. The teams getting the best results treat it as a complement to real data, not a shortcut around collection and curation. Know which scenario you’re in before you start generating.