AI
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.
What it is
Synthetic glossaryDataData is raw, uninterpreted information collected and stored so it can be analysed, processed, or used to inform decisions. On its own it carries no meaning; context and interpretation are what make it useful.Open glossary term is artificially generated data that mimics the characteristics of real data without being directly derived from real people or events.
In AI development, synthetic glossaryDataData is raw, uninterpreted information collected and stored so it can be analysed, processed, or used to inform decisions. On its own it carries no meaning; context and interpretation are what make it useful.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, uninterpreted information collected and stored so it can be analysed, processed, or used to inform decisions. On its own it carries no meaning; context and interpretation are what make it useful.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, uninterpreted information collected and stored so it can be analysed, processed, or used to inform decisions. On its own it carries no meaning; context and interpretation are what make it useful.Open glossary term is a useful approach.
It is most relevant when:
It is less relevant when:
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
The basic mechanism
Synthetic glossaryDataData is raw, uninterpreted information collected and stored so it can be analysed, processed, or used to inform decisions. On its own it carries no meaning; context and interpretation are what make it useful.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, uninterpreted information collected and stored so it can be analysed, processed, or used to inform decisions. On its own it carries no meaning; context and interpretation are what make it useful.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, uninterpreted information collected and stored so it can be analysed, processed, or used to inform decisions. On its own it carries no meaning; context and interpretation are what make it useful.Open glossary term should meet.
Reviewing synthetic glossaryDataData is raw, uninterpreted information collected and stored so it can be analysed, processed, or used to inform decisions. On its own it carries no meaning; context and interpretation are what make it useful.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 pattern is a reusable solution to a design problem that recurs across products and contexts. It captures an approach already shown to work, so a team solves the problem once rather than every time it appears.Open glossary term.
What to look for
Focus on:
Where it goes wrong
Most issues come from: Generating large volumes of low-quality synthetic glossaryDataData is raw, uninterpreted information collected and stored so it can be analysed, processed, or used to inform decisions. On its own it carries no meaning; context and interpretation are what make it useful.Open glossary term is not a shortcut. It makes the problem worse.
What you get from it
Understanding synthetic glossaryDataData is raw, uninterpreted information collected and stored so it can be analysed, processed, or used to inform decisions. On its own it carries no meaning; context and interpretation are what make it useful.Open glossary term gives you:
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.
Get in touch
Generated data fills gaps convincingly, which is exactly what makes it risky. We can help you work out where it helps and where it misleads.
No guesswork. No assumptions. Just data you can trust to stand in.
FAQ
Common questions
A few practical answers to the questions that usually come up around this method.
What is synthetic data in AI?
Synthetic data 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 datasets where real data is limited, expensive, or raises privacy concerns.
Can synthetic data replace real data?
Not entirely. Synthetic data 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 pipelines usually combine both.
Is synthetic data safe from a privacy perspective?
Generally yes, because it is not derived from real individuals. However, if synthetic data is generated from a model trained on real data, there is a risk that it encodes patterns 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 models or specialised data generation tools. But the specification of what to generate (what types of examples, what edge cases, 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 data does not reflect the real variability of user inputs, the model trained on it will have gaps. If the generation process 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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