Synthetic data is artificial data generated by rules, simulations, statistical models, or generative AI rather than collected from real-world events. Teams use it to train and test machine learning models when real data is scarce, private, or costly to label. Its value depends on how it is generated, checked, and mixed with real data.
Key Takeaways
- Synthetic data types differ in how much real data they keep: fully synthetic data keeps none, partially synthetic data replaces sensitive values in a real dataset, and hybrid data pairs real records with generated ones.
- Specification-based generation builds a synthetic dataset from a description of the behavior a model should learn, so a team with no example data for a capability can still start.
- Provider terms can restrict what a team does with generated data, because some frontier model providers do not permit training on data generated with their models.
What Is Synthetic Data?
Synthetic data, also called artificial data, is data produced by rules, simulations, statistical models, or generative AI rather than observed in the real world. Synthesizing data means generating new records that follow the structure and patterns of the cases they represent, such as a card transaction or a patient question. A synthetic dataset is a collection of those records.
Synthetic data has two common roles in AI work: standing in for real data that is private or hard to obtain, and supplying training examples for capabilities that no collected data covers.
Three properties separate useful synthetic data from random values:
- Structure: records follow the format a system expects, such as table rows, chat turns, or images.
- Realism: values and relationships stay close to the cases they represent.
- Coverage: the data includes rare cases that collected data seldom contains.
What Are the Types of Synthetic Data?
Synthetic data is classified by how much real data it contains and by its format. Fully synthetic data contains no real records, partially synthetic data replaces selected values in a real dataset, and hybrid data combines real records with generated ones.
| Type | What It Contains | Typical Use |
|---|---|---|
| Fully synthetic | Generated records only, built to match the patterns or behavior of the cases they represent | Training models when no real data exists, or sharing data without real records |
| Partially synthetic | A real dataset in which sensitive values, such as names and contact details, are replaced with generated ones | Analysis that needs real structure but not real identities |
| Hybrid | Real records combined with fully synthetic records | Expanding a small real dataset while keeping its real signal |
Synthetic data can be produced in various forms:
- Text: instruction and response pairs, conversations, and documents
- Tabular: rows and columns, such as transactions or patient records
- Images and video: rendered or generated scenes for vision models
- Time series: generated sensor readings or event sequences
How Is Synthetic Data Generated?
Synthetic data is generated by four families of methods: rule-based simulation, statistical sampling, learned generative models, and large language model (LLM) generation. Each starts from a different input, which determines where it works and where it fails.
| Method | Starting Point | Suited To | Main Limit |
|---|---|---|---|
| Rules and simulation, including agent-based models | A hand-built model of how a system behaves | Traffic, disease spread, physics, software test data | Only as realistic as the rules |
| Statistical sampling | Distributions and correlations fitted to real data | Tables and time series with well-understood structure | Misses relationships the model does not capture |
| Learned generative models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs) | A model trained on real data | Images, sensor data, complex tables | Needs real training data and skill to evaluate |
| LLM generation | Prompts, seed examples, or a described behavior | Text, such as instructions, dialogue, and preference pairs | Repetition as dataset size grows |
How Do Seed-Based and Specification-Based Generation Differ?
Seed-based generation expands a small pool of human-written examples, while specification-based generation starts from a description of the target behavior. Most synthetic data techniques assume a team starts with a seed pool of prompts or an existing dataset to improve, but often, teams have no example data at all.
An example of a specification-based synthetic data generator is Invent a Dataset. It builds a training-ready dataset from a description of the behavior a model should learn, with no seed data, schema, or labeling guide. A team describes a task, such as summarizing business documents to an organization’s standards or classifying customer requests across departments, and sets dataset size and scope in the same flow.
Why Does Synthetic Data Matter for AI?
Synthetic data matters for AI because the quality of a model’s output is dependent on the quality of the data it is trained on. Often, the data a team needs does not exist, cannot be shared, or costs too much to label. Synthetic data AI workflows address three constraints:
- Scarcity: rare events such as fraud, uncommon conditions, and edge cases appear too seldom in collected data, and generation can add them on demand.
- Privacy: teams can develop and share data that holds no real customer or patient records.
- Labeling cost: generated examples arrive with labels or completions attached, which reduces manual annotation.
Customization is the larger reason many organizations use synthetic training data. General-purpose models are built for broad use, and a company’s own policies, tone, and workflows rarely appear in public data in the form a model needs. Synthetic data lets a team specify that behavior directly. A bank can describe how it reviews a disputed charge, a health system can describe its intake protocols, and a law firm can specify the jurisdictions it practices in.
What Is Synthetic Data Used For?
Synthetic data is used wherever real data is rare, restricted, or expensive to label, including fraud detection, clinical research, vehicle safety testing, manufacturing inspection, software testing, and language model training.
| Field | What Teams Generate | Why Synthetic Data Fits |
|---|---|---|
| Finance | Suspicious transaction patterns and risk scenarios | Fraud examples are rare in collected data |
| Healthcare | Patient records, clinical question-and-answer pairs, and medical images | Records are private, and rare conditions have few examples |
| Automotive | Driving scenes and crash scenarios | Real crash data is costly and dangerous to collect |
| Manufacturing | Defect images and sensor readings | Defects occur too seldom to photograph at volume |
| Software testing | Realistic records for development and performance tests | Developers can work without access to personal data |
| Language model training | Instruction and response pairs, dialogue, preference pairs, and multilingual examples | Needed behaviors are missing from collected data |
How Is Synthetic Data Different From Real, Anonymized, and Simulated Data?
Synthetic data differs from real data in origin, from anonymized data in content, and from simulated data in scope, since simulation is one method of producing synthetic data.
| Data Type | Origin | Contains Real Records? | Main Risk |
|---|---|---|---|
| Real data | Observed events | Yes | Privacy limits, access limits, labeling cost, coverage gaps |
| Anonymized data | Real data with identifiers removed | Yes, minus identifiers | Re-identification from the remaining fields |
| Partially synthetic data | Real data with sensitive values replaced | Yes, in unchanged fields | Disclosure through the values left in place |
| Fully synthetic data | Generated by a model or rules | No | Bias or errors inherited from the generator |
| Simulated data | Output of a model of a system | No | Realism limited by the model |
What Are the Risks of Synthetic Data?
The main risks of synthetic data are inherited bias, model collapse, privacy obligations that still apply, and unverified quality. You can reduce each through how you generate, filter, and mix the data with real data.
| Risk | What Happens | How To Reduce It |
|---|---|---|
| Inherited bias | A generator trained on skewed records generates skewed records | Audit the generator and its source records |
| Model collapse | Repeated training on model output loses rare examples | Mix in real data and avoid training on a model’s own output in loops |
| Privacy obligations | Personal data rules can still apply | Assess re-identification risk and check the applicable rules |
| Unverified quality | Errors and repeated patterns in the data carry into the model | Check accuracy, duplicates, and constraint violations before training |
What Makes Synthetic Training Data High Quality?
High-quality synthetic training data is accurate, diverse, task-relevant, and consistent with the constraints a team sets. Four checks apply:
- Accuracy: facts, labels, and completions are correct.
- Diversity: examples differ in meaning and wording, with few duplicates.
- Constraint following: length, format, tone, persona, and language match the request.
- Downstream testing: you train a model on the data and compare it with baselines on held-out prompts.
How Do You Keep Synthetic Data Diverse at Scale?
Diversity collapse is the pattern in which generated examples grow more repetitive as the requested volume rises. Adaption’s October 2026 technical report evaluated Invent API, the programmatic interface to Invent a Dataset, against five frontier model APIs and found that Invent’s datasets maintained the highest diversity and quality simultaneously.
Can You Use Synthetic Data to Train Your Own Model?
It depends on the provider’s terms. Some frontier model providers do not permit training on data generated with their models, while some open-weight model licenses do permit it, so a team should read the terms for every model it generates data with.
Three checks apply before training on generated data:
- Terms of service: confirm whether the provider allows outputs to be used to train another model.
- Model licenses: read the license of each open-weight model, because permissions differ.
- Commercial use: confirm the terms cover commercial deployment of the trained model, not only research.
The terms decide who controls the result. A dataset a team may train on commercially is an asset the team can reuse across models, while restricted terms can limit what a team does with every model trained on the data. Adaption explicitly permits commercial use of datasets it generates for downstream model training.
How Can Enterprise Teams Generate Synthetic Data and Train a Model with Adaption?
Enterprise teams generate synthetic data and train a custom model by pairing Invent a Dataset with AutoScientist, Adaption’s system for automating the research loop behind model training. Invent a Dataset generates a training-ready dataset from a description of the behavior a model should learn, and AutoScientist co-optimizes a model’s training data and training recipe against an outcome the team sets.
Together, they give a team a zero-data path to a custom model that the team owns, with no seed corpus, schema, or labeling guide needed.
Author
Adaption TeamDate