Synthetic Data

Q: Can training on synthetic data make AI worse?

Yes - this is a documented risk called 'model collapse' or 'data poisoning by synthetic feedback.' When models are trained repeatedly on outputs from similar models, they can amplify systematic errors and gradually lose capability on rare or unusual cases. The risk increases when synthetic data is used without quality filtering or when it displaces rather than supplements real data. Careful curation and diversity in training sets are the main mitigations.

Latest update: 26/05/03

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Definition

Synthetic data is artificially generated training data – created by an AI model rather than collected from the real world – used to train, fine-tune, or test other AI systems when real data is scarce, sensitive, or expensive to obtain.

What Is Synthetic Data?

Training AI models requires data – ideally a lot of it, with good coverage of the scenarios the model will face. But real-world data isn’t always available. Medical records are private. Edge cases are rare. Annotating examples by hand is slow and expensive. Synthetic data solves the supply problem by generating training examples artificially.

In AI development, a capable model generates realistic examples – text, conversations, labeled pairs, scenarios – that another model can learn from. The generated data isn’t collected from real events; it’s fabricated to look real enough to be useful for training.

It’s a way of bootstrapping capability when the data you need doesn’t exist yet, or can’t be used.

💡 How Does It Work?

A synthetic data pipeline typically starts with a capable model – often a large frontier model – and a set of instructions describing what kinds of examples to generate. The model produces samples: question-answer pairs, labeled documents, edge case scenarios, translated text, or whatever training format the downstream model needs.

Think of it like a flight simulator. A real flight produces real experience, but you can’t deliberately manufacture a thousand near-miss scenarios safely. A simulator generates those scenarios on demand – artificial, but realistic enough that pilots learn real skills from them. Synthetic data does the same thing for AI: manufactured, but designed to teach real capabilities.

The synthetic examples get reviewed – sometimes by humans, sometimes by another AI – and filtered for quality before going into the training set.

Why It Matters for Your Prompts

Most users don’t interact with synthetic data directly. But it shapes the AI you’re using every day. A significant portion of the training data behind modern fine-tuned models includes AI-generated examples – including examples of the specific task types those models were built to handle.

Where this becomes practically relevant: if you’re building AI applications and need to fine-tune a model, synthetic data generation is a legitimate and cost-effective path to creating training sets. Instead of paying humans to annotate thousands of examples, you can prompt a capable model to generate them – then review and filter for quality.

It also raises a question about data quality. AI-generated training data can introduce systematic patterns, biases, or errors that propagate into the trained model in ways that human-generated data wouldn’t. The more a model is trained on its own or similar models’ outputs, the more those patterns compound. It’s a real limitation – not a reason to avoid synthetic data, but a reason to use it thoughtfully.

🌐 Real-World Example

A healthcare company wants to fine-tune an AI model to answer common patient questions about post-surgical care. The ideal training data would be real patient conversations – but those are private, legally sensitive, and expensive to collect and annotate.

Instead, they generate synthetic conversations. A clinical team writes 50 real sample questions and ideal answers. They prompt a capable model to expand those into 5,000 varied examples: different phrasings, different patient demographics, different question styles, covering the same clinical content.

A nurse reviews a sample for clinical accuracy. The synthetic dataset gets used for fine-tuning. The resulting model handles post-surgical queries accurately – trained entirely on fabricated conversations that were realistic enough to teach the right patterns.

Related Terms

Fine-Tuning – Synthetic data is most commonly used as training material for fine-tuning; the two techniques are closely linked in practice.
Model Distillation – Distillation often relies on synthetic data generated by the teacher model to train the student, making the two techniques natural partners.
RLHF – Synthetic preference pairs – AI-generated examples of better and worse responses – can supplement human-labeled data in RLHF pipelines.
Hallucination – Synthetic data generated by a model that hallucinates can embed those errors into fine-tuned models – a key quality risk to manage when building synthetic datasets.
Prompt Template – Generating good synthetic data at scale usually requires well-designed prompt templates that produce consistent, high-quality examples reliably.

Encyclopedia Fundamental Prompting Techniques Architecture & Technical

Frequently Asked Questions

Is synthetic data as good as real data for training AI?

It depends on the task and the quality of the generation process. For tasks with well-defined correct answers – classification, format adherence, domain-specific Q&A – high-quality synthetic data can be nearly as effective as real data. For tasks requiring human judgment, diversity of lived experience, or rare real-world edge cases, real data still has an edge. Most serious training pipelines use a mix of both.

Can training on synthetic data make AI worse?

Yes – this is a documented risk called “model collapse” or “data poisoning by synthetic feedback.” When models are trained repeatedly on outputs from similar models, they can amplify systematic errors and gradually lose capability on rare or unusual cases. The risk increases when synthetic data is used without quality filtering or when it displaces rather than supplements real data. Careful curation and diversity in training sets are the main mitigations.

Do AI companies use synthetic data in their own training?

Yes – most do, to varying degrees. Anthropic, OpenAI, Google, and Meta have all discussed using AI-generated data as part of their training pipelines, particularly for fine-tuning and alignment work. The details are rarely disclosed, but synthetic data generation is now standard practice in model development, not an edge case.

How much human review does synthetic data need?

More than people typically assume. Raw AI-generated examples often contain subtle errors, inconsistencies, or stylistic patterns that degrade training quality if left unchecked. The practical rule of thumb: the higher the stakes of the downstream model, the more review the synthetic data needs. For low-stakes formatting tasks, light spot-checking may suffice. For medical, legal, or safety-relevant applications, expert review of a meaningful sample is worth the cost.

References

Gunasekar, S. – Textbooks Are All You Need – Demonstrates that small models trained on high-quality synthetic data can perform surprisingly well, making the case for data quality over data volume.
Shumailov, I. – The Curse of Recursion: Training on Generated Data Makes Models Forget – Documents the model collapse risk from training iteratively on synthetic data without real-data grounding.