Generate test data with generative AI by first defining the behavior and scenarios you need to test, then specifying a schema and constraints, choosing whether you need sample values, a reusable generator, or synthesized tables, and validating every output before use. AI-generated data can be useful, but “synthetic” does not automatically mean private, representative, or correct.
Start with the test objective, not the prompt
A model cannot infer every hidden business rule from a vague request. Before generating anything, write down the behavior under test, the inputs it accepts, and the outcomes the application should produce. Include ordinary cases as well as boundaries, invalid inputs, and rare combinations.
For example, a checkout test might need a valid order, an order at the maximum permitted quantity, an invalid postal code, and a payment decline. Specify what each case is meant to prove; “make realistic checkout data” does not define expected behavior or coverage.
- Behavior: What application feature or decision are you testing?
- Scenarios: Which normal, boundary, invalid, and unusual combinations matter?
- Expected results: What should pass, fail, or trigger a particular response?
- Data use: Is this for a one-off test, a repeatable test suite, or a larger multi-table environment?
Choose the right kind of generated output
There are three common targets: values to paste into a test, code that generates values repeatedly, or synthetic rows based on source tables. A test-generation product may also populate inputs into generated test cases. These approaches solve different problems; available sources do not establish one universal best option or an independent head-to-head winner.
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| Approach | Input basis | Output | Useful when | Watch for |
|---|---|---|---|---|
| Prompted values | Your written schema, constraints, and examples | Individual values or a small dataset | You need a small, isolated fixture quickly | Formatting drift, missing cases, and inconsistent reruns |
| Generated program | Your specification, optionally a faker library | Reusable generator code | You need repeatable test fixtures integrated into a pipeline | Review and test the code; generated code can encode incorrect assumptions |
| Warehouse-native synthesis | Source tables and their structure | Artificial rows shaped to source columns | You need structured datasets or related tables | Product edition, join consistency, privacy controls, and source-data handling |
| Generated test-case population | Captured patterns or test-case context in a specific product | Values filled into test cases | You already use a compatible test-generation workflow | Product-specific modes and configuration can limit availability |
Prompted values for small fixtures
Ask for a strict machine-readable format and enumerate field rules. For example, request JSON objects with an integer quantity in a stated range, an enumerated status, and an ISO-formatted date. Request invalid and boundary examples explicitly rather than expecting the model to include them.
Generator code for repeatability
For fixtures that must be recreated, ask for a generator program or use a faker-backed library and review the result. Require a seed or other deterministic mechanism if repeatability matters, and test the program against the same schema and business rules as the generated values. Research describes prompting an LLM to produce raw data, generator code, or code that uses faker libraries as distinct targets (2024 research preprint).
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Synthetic rows for structured tables
When tables, data types, and relationships matter, a source-aware workflow can be more suitable than asking a chat model to invent a flat list. Snowflake documents synthetic output using source column names and types, and describes join-key treatment for consistency across tables. Its procedure requires Enterprise Edition or higher; consult the Snowflake user guide and procedure reference for current behavior.
Values inside generated test cases
Katalon TrueTest documents environment-level modes named Disabled, Raw, Raw with PII mocked values, and Synthetic. Its documentation says Disabled is the default, modes are configured by tracking environment, and users must contact TrueTest support to switch modes. This is a product-specific captured-test-case workflow, not a general-purpose dataset synthesizer (Katalon documentation, last updated December 2025).
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Define a schema and constraints before generating
Give the generator enough structure to produce data that the test can actually use. Include field types, required versus nullable fields, formats, allowed values, ranges, uniqueness, relationships, and cross-field rules. Use non-sensitive examples where possible.
- Types and nullability: State whether each field is a string, integer, date, boolean, or another exact type, and whether null is permitted.
- Formats and ranges: Define date formats, length limits, numeric bounds, and accepted patterns.
- Enumerations: List exact allowed values rather than asking for a plausible status or category.
- Relationships: Specify foreign keys, stable identifiers, and how records in related tables connect.
- Cross-field invariants: State rules such as an end date following a start date or a paid order having a payment reference.
- Coverage counts: Request enough cases for each scenario, including invalid and boundary combinations.
For related tables, define how keys should remain consistent. Snowflake describes join-key handling and a consistency secret for generating consistent keys across tables or runs; those are product-specific capabilities, not a general guarantee from generative AI.
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Generate and validate in a controlled workflow
- Specify the test need. Record the behavior, scenarios, fields, and expected results before calling a model or configuring a product.
- Choose the output shape. Decide whether a test needs individual values, reusable generator code, source-shaped tables, or inputs filled into generated test cases.
- Provide constraints and examples. Use a schema, relationship rules, and non-sensitive examples. Avoid sending production personal data unless your organization has approved that use and its controls.
- Control access and retention. Decide what prompt and output data is sent to an external model or service, who can access it, where generated files are stored, and how long they are retained.
- Parse and validate. Reject malformed output; enforce types, required fields, business rules, referential integrity, and uniqueness using code or database constraints.
- Check test coverage. Verify that the dataset contains the intended ordinary, boundary, invalid, and rare scenarios. Realistic-looking values do not prove that the right behavior is covered.
- Review privacy risk. Check for outputs resembling sensitive records and assess whether auxiliary information could enable re-identification before the data reaches a less-controlled environment.
- Rerun when required. If reproducibility matters, record the prompt or configuration and seed where supported, then verify that a new run meets the same contract.
- Reassess after changes. Re-check when the model, source data, prompt, test objective, or downstream use changes.
AWS lists holdout data, human evaluation, adversarial testing, and synthetic data to fill dataset gaps among possible generative-AI evaluation practices; these are evaluation methods, not a single validated test-data quality score (AWS testing guidance). The UK government advises testing through development and after launch and says, “Where possible, conduct tests with anonymised or synthetic data” (Data and AI Ethics Framework).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Do not treat synthetic data as automatically private
Generated records can still expose information. Risk depends on what data was used as input or training material, what the model can reproduce, and whether outside information could identify a person. The UK government framework warns that AI can re-identify people believed to be anonymised by linking information. An LLM can also unintentionally generate values that match sensitive records; the ISTQB sample answer notes this possibility without providing an empirical probability (ISTQB sample answer, version 1.1, dated 27 April 2026).
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Use a risk-based review rather than relying on the “synthetic” label. Consider whether sensitive data was supplied, who can see the prompts and outputs, whether records resemble real individuals, and whether the dataset will be shared or retained. Snowflake documents an optional similarity filter that removes rows based on nearest-neighbor distance ratio and distance-to-closest-record measures. That mechanism is not a blanket privacy guarantee; evaluate its fit to your threat model. Snowflake also warns that enabling the filter causes failure when non-string columns contain nulls.
If the system under test is itself an AI model, keep test data distinct from training, validation, and evaluation sets so that results are meaningful. The Australian Government AI Technical Standard discusses this separation and synthetic data as a possible way to supplement dataset completeness, while also noting retention of sensitive data for bias testing (AI Technical Standard).
Product and operational fit
Capabilities differ by environment, data volume, and product edition. Snowflake documents schema-shaped synthetic output and optional similarity filtering, but its synthetic-data procedure requires Enterprise Edition or higher. Katalon TrueTest describes captured-pattern modes for test-case inputs, with the configuration limits noted above. Infosys describes test-data-management services combining privacy and compliance assessment, masking, test-data mining and provisioning, synthetic generation, and database virtualization; IRI describes RowGen for referentially correct test data in production-like formats, but its cited page does not substantiate a generative-AI feature. These vendor descriptions establish offered approaches, not independent comparative validation (Infosys service page; IRI solution page).
Common problems and fixes
- Output will not parse: Require one strict format, such as a JSON array with no surrounding commentary; parse it immediately and reject invalid results rather than repairing silently.
- Values violate the schema: Validate types, ranges, formats, nullability, and enumerations in code. Tighten the specification and regenerate only the rejected records if your workflow permits.
- Related records do not join: State key relationships explicitly or use a source-aware workflow designed to preserve join consistency; do not assume independently prompted tables will align.
- Rare cases are missing: Name each target scenario and expected count in the request, then check coverage as a separate validation step.
- Generated values are too similar to real data: Review privacy exposure and similarity risk, reduce sensitive inputs, and use appropriate organizational controls. A similarity filter can help with a particular mechanism but does not establish anonymity.
- Snowflake similarity-filter run fails: Check for nulls in non-string columns when the filter is enabled, as Snowflake documents that condition as a failure case.
- TrueTest mode is unavailable: Confirm the tracking environment and current mode; Katalon says Disabled is the default and switching modes requires contacting TrueTest support.
- Reruns produce different fixtures: Use a deterministic seed or stable generation configuration where supported, and retain the specification and validation results needed to reproduce a test.
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