Synthetic Data

A practical guide to understanding what synthetic data is and how it is used in AI development.

What synthetic data is, how it is generated, and what product and design teams need to know about its role in training, testing, and evaluation.

22 May 20264 min read

What it is

In AI development, synthetic glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term is used to create training examples, evaluation glossaryDatasetA dataset is a structured collection of data used for analysis, training models, or processing.Open glossary term, and test cases where real data is unavailable, too limited, or raises privacy concerns.

For example, a company building an AI glossaryFeatureA feature is a specific piece of functionality within a product that delivers value to users. It represents something users can do or experience as part of the overall product.Open glossary term for processing customer support glossaryRequestA request is an action sent from a client to a server asking for data or a service.Open glossary term might generate thousands of synthetic support conversations to train and evaluate the glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term, rather than using real customer data that would require careful anonymisation and consent management.

Synthetic glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term can be generated by glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term themselves — asking a language model to produce examples of a particular type of input — or through other simulation and generation techniques.

When to use it

Understand when synthetic glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term is a useful approach.

It is most relevant when:

Real training data is scarce or expensive to collect

Privacy regulations restrict the use of real user data

You need a large volume of examples for a specific, well-defined task

You want to generate test cases for edge cases that rarely appear in real data

Evaluation datasets need to cover scenarios that have not yet been observed

It is less relevant when:

Real data is abundant and its use is permissible

The task requires the nuance and variability of genuine human inputs

The synthetic data generation process introduces its own biases

Key takeaway

Synthetic data is a practical tool for supplementing real data — but the quality and representativeness of what is generated determines its value.

How it works

Understand the basic mechanism. Synthetic glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term generation typically starts with a clear specification of what the data should look like — the types of inputs, the range of topics, the expected outputs.

A language glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term can then be used to generate examples matching that specification at scale. These examples are reviewed for quality, filtered for relevance, and used in training or evaluation.

The risk is that if the generator and the glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term being trained share the same underlying glossaryBiasBias is a systematic distortion in thinking or data that affects the accuracy of research or decision-making.Open glossary term, synthetic glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term will reinforce rather than expand capability.

What this means for designers and product teams. Product and design teams contribute most in the specification phase — defining what good examples look like, what glossaryEdge CaseAn edge case is a rare or extreme scenario that falls outside typical user behaviour.Open glossary term should be covered, and what quality standards the generated glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term should meet.

Reviewing synthetic glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term for quality and representativeness is also a product responsibility. Poor synthetic data is worse than no data, because it trains the glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term on incorrect or misleading glossaryPatternA reusable solution to a common design problem.Open glossary term.

What to look for

Focus on:

Specification quality — whether the generation brief is clear enough to produce useful examples

Coverage — whether the synthetic data covers the full range of inputs the model will encounter

Diversity — whether examples are varied enough to avoid reinforcing narrow patterns

Quality — whether generated examples are realistic and meet the required standard

Bias risk — whether the generation process introduces systematic distortions

Where it goes wrong

Most issues come from: Generating large volumes of low-quality synthetic glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term is not a shortcut — it makes the problem worse.

Vague specifications that produce generic or unhelpful examples

No quality review of generated data before use

Over-reliance on synthetic data in domains where real-world variability matters

Circular bias where the model generates data that reflects its own limitations

What you get from it

Understanding synthetic glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term gives you:

A practical approach to data scarcity problems in AI development

A privacy-preserving alternative to using real user data in some contexts

Better ability to contribute to training data specification and evaluation

More informed decisions about when synthetic data is and is not appropriate

Key takeaway

Synthetic data is only as useful as its specification and quality review. Generated in volume without care, it creates more problems than it solves.

FAQ

Common questions

A few practical answers to the questions that usually come up around this method.

What is synthetic data in AI?

Synthetic glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term is artificially generated data that mimics the characteristics of real data without being derived from real people or events. It is used to create training examples, test cases, and evaluation glossaryDatasetA dataset is a structured collection of data used for analysis, training models, or processing.Open glossary term where real data is limited, expensive, or raises privacy concerns.

Can synthetic data replace real data?

Not entirely. Synthetic glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term can supplement real data and help cover gaps, but it typically lacks the unpredictability and genuine variability of real-world inputs. The best AI training glossaryPipelineA pipeline is a sequence of automated steps that process code or data from start to finish.Open glossary term usually combine both.

Is synthetic data safe from a privacy perspective?

Generally yes, because it is not derived from real individuals. However, if synthetic glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term is generated from a glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term trained on real data, there is a risk that it encodes glossaryPatternA reusable solution to a common design problem.Open glossary term that could be traced back to real people. Privacy review is still advisable for sensitive domains.

Who generates synthetic data?

Often engineers or ML teams, using language glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term or specialised glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term generation tools. But the specification of what to generate — what types of examples, what glossaryEdge CaseAn edge case is a rare or extreme scenario that falls outside typical user behaviour.Open glossary term, what quality looks like — is a product and design input. Both sides need to be involved.

What are the risks of using synthetic data?

The main risks are quality and representativeness. If the generated glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term does not reflect the real variability of user inputs, the glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term trained on it will have gaps. If the generation glossaryProcessA process is a defined sequence of steps used to achieve a specific outcome.Open glossary term introduces systematic biases, those will be encoded in the model. Both require careful review.

Quick take

Synthetic data lets you train and test AI systems without relying entirely on real user data — which matters for privacy, scale, and speed.

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