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Generate test data with generative AI by first defining the behavior to test, the data schema, and the rules each record must satisfy. Then choose whether you need a few values, reusable generator code, a full dataset, or inputs for generated test cases. Validate the result before use: AI-generated data can be malformed, unrepresentative, or similar to sensitive real records. “Synthetic” does not automatically mean private, correct, or suitable for your test.
Start with the test objective, not the prompt
Write down what the system should do and what the test needs to prove. A request such as “make realistic users” leaves the model to guess the fields, business rules, and edge cases. Specify those yourself.
- Behavior: What feature, API, workflow, or failure path is under test?
- Scenarios: Include ordinary cases, boundaries, invalid inputs, and rare combinations relevant to the behavior.
- Expected outcomes: State what the application should accept, reject, calculate, or display for each case.
- Data requirements: List fields, types, nullability, allowed values, formats, uniqueness, relationships, and cross-field rules.
- Use constraints: Note volume, repeatability, environment, and whether the data will be shared or retained.
For example, a checkout test may need one valid order, an order at the maximum permitted quantity, an expired payment token, and an address with a missing postal code. The objective determines whether the data should look ordinary or deliberately violate a rule.
Choose the form of data you need
Generative AI can produce raw values, a reusable generator program, or code that uses a faker library; these are different output targets described in a 2024 preprint on LLM test-data generation (paper). A tool may also populate inputs in generated test cases, while a warehouse-native workflow can create synthetic rows shaped around source tables.
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| Approach | Best fit | Trade-off to check |
|---|---|---|
| Prompt for individual values | A small, isolated input or a few hand-reviewed scenarios. | Convenient to inspect, but output may vary between requests and needs parsing and validation. |
| Prompt for generator code | A repeatable dataset or a test pipeline that can run locally. | Code still needs review, execution in a controlled environment, and checks for schema and rule violations. |
| Use a faker-backed generator | Many ordinary values in familiar formats, produced by code. | Faker-style values do not automatically satisfy application-specific relationships, distributions, or edge cases. |
| Use warehouse-native synthesis | Rows based on existing tables where column types and relationships matter. | Requires suitable platform access and careful privacy and output validation. |
| Use a test-case tool to populate inputs | Teams whose tests are created within that product’s workflow. | Behavior and configuration are product-specific; it is not necessarily a general-purpose dataset generator. |
Give the model a schema and explicit rules
Use a strict output format when asking for values. Include constraints the model cannot safely infer, such as whether identifiers must be unique, whether two dates must be ordered, and which combinations are invalid. Prefer non-sensitive examples and synthetic identifiers in the prompt.
Example prompt for JSON test cases
Generate exactly 5 test cases for a checkout API. Return only a JSON array; do not include prose or markdown.Each object must have these fields and types:
- case_id: string, unique within this response
- quantity: integer from 1 to 20
- payment_token: string
- expected_status: integer
- scenario: one of "valid", "boundary", "invalid"
Rules:
- Include one ordinary valid purchase with expected_status 200.
- Include quantity 20 as a valid boundary case with expected_status 200.
- Include quantity 0 as invalid with expected_status 400.
- Include an expired payment token as invalid with expected_status 402.
- Include one additional case that tests a missing payment token with expected_status 400.
- Use invented tokens beginning with "test_"; do not use real payment credentials.Do not treat a syntactically valid response as a correct one. Parse it, check every stated rule in code, and run the cases against the system’s actual expected behavior. For a larger or recurring dataset, request a generator program rather than a long list of model-written records, then review and test that program.
Generate repeatable values with code
When the goal is simple structured cases, a small deterministic program can be easier to validate than asking a model to emit a large output. The example below uses Python’s standard library, fixes its seed for repeatability, and creates cases that deliberately cover valid, boundary, and invalid conditions. It is illustrative: change field names and expected outcomes to match the API contract you are testing.
import json
import random
rng = random.Random(41)
cases = [
{
"case_id": "case_valid_001",
"quantity": rng.randint(1, 19),
"payment_token": "test_valid_001",
"expected_status": 200,
"scenario": "valid",
},
{
"case_id": "case_boundary_001",
"quantity": 20,
"payment_token": "test_valid_002",
"expected_status": 200,
"scenario": "boundary",
},
{
"case_id": "case_invalid_quantity_001",
"quantity": 0,
"payment_token": "test_valid_003",
"expected_status": 400,
"scenario": "invalid",
},
{
"case_id": "case_expired_payment_001",
"quantity": 1,
"payment_token": "test_expired_001",
"expected_status": 402,
"scenario": "invalid",
},
{
"case_id": "case_missing_payment_001",
"quantity": 2,
"payment_token": "",
"expected_status": 400,
"scenario": "invalid",
},
]
assert len({case["case_id"] for case in cases}) == len(cases)
assert all(1 <= case["quantity"] <= 20 for case in cases if case["scenario"] != "invalid")
print(json.dumps(cases, indent=2))The fixed seed makes this program’s randomized ordinary quantity repeatable. It does not make an LLM response deterministic, prove that the cases represent production behavior, or replace tests for rules not encoded here. If you use a faker-backed library, constrain its outputs and add explicit logic for cross-field rules and edge cases; plausible names and addresses are not the same as valid application scenarios.
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Use source-table synthesis when relationships matter
If the test requires several related tables, hand-prompted records can easily disagree about shared identifiers or foreign keys. A warehouse-native synthesis feature may be a better fit when it preserves source columns and types and supports consistent join keys. It still needs review for the test’s specific constraints and for privacy risk.
Snowflake’s documented workflow
Snowflake documents GENERATE_SYNTHETIC_DATA for producing a table with source column names and types and statistically similar artificial values. Its documentation distinguishes statistical fields, categorical strings, and non-categorical strings; the latter are redacted unless a replacement output format is specified. Join-key handling and a consistency secret can support consistent keys across runs or tables. The procedure requires Enterprise Edition or higher. These are documented product behaviors, not a guarantee that generated rows are safe or correct for every test (user guide; procedure reference).
Snowflake also documents an optional similarity filter based on nearest-neighbor distance ratio and distance to the closest record. If enabled, nulls in non-string columns cause failure. A similarity filter is a specific control, not a complete privacy guarantee; assess the output and threat model for the intended use.
Katalon TrueTest’s captured-test workflow
Katalon documents Disabled, Raw, Raw with PII mocked values, and Synthetic modes for populating test cases. Its page describes Synthetic mode as using an AI-based model to generate realistic values based on captured patterns. Modes are configured by tracking environment; Disabled is the default, and the documentation says changing modes requires contacting TrueTest support. This is a product-specific test-case workflow, not a general-purpose dataset synthesizer. The documentation page says it was last updated in December 2025 (Katalon documentation).
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Validate generated data before using it
Validation should be executable wherever possible, not a visual spot-check alone. AWS guidance lists holdout data, human evaluation, adversarial tests, and synthetic data to fill dataset gaps among possible evaluation practices; it does not define a single universal test-data quality score (AWS testing guidance).
- Format: Parse the output and reject malformed JSON, missing fields, unexpected types, or extra records.
- Schema: Enforce required fields, nullability, formats, ranges, allowed categories, and length limits.
- Business rules: Check cross-field conditions, such as start dates preceding end dates or totals matching line items.
- Relational integrity: Verify uniqueness, foreign keys, and consistent shared keys across tables.
- Coverage: Confirm every requested scenario is present, including invalid, boundary, and rare combinations; do not equate realistic-looking values with coverage.
- System behavior: Run the cases against the application and compare actual results with expected outcomes.
- Repeatability: If tests depend on stable fixtures, rerun generation and verify that the output is reproducible or that a versioned artifact is retained.
- Privacy: Check for sensitive-looking values or plausible matches to real records, especially when source data or personal information informed generation.
Protect privacy and control the data lifecycle
Do not call data anonymous just because an AI generated it. Risk depends on the inputs, model and service context, generated outputs, access controls, and what other information can be linked to those outputs. The UK government’s Data and AI Ethics Framework warns that AI can re-identify people believed to be anonymised by linking information, and recommends risk-based controls. It advises: “Where possible, conduct tests with anonymised or synthetic data.” It also says testing should continue throughout build phases and be repeated after a service goes live (framework).
- Decide whether prompts or source data contain personal, confidential, or regulated information before sending them to an external service.
- Limit who can access prompts, generated output, and stored test fixtures; set retention and deletion rules appropriate to the data.
- Use non-sensitive examples where possible, and do not assume a similarity filter catches every privacy risk.
- Reassess the data when the model, source tables, prompts, access arrangements, or downstream use changes.
- If the system under test is itself an AI model, keep test data distinct from training, validation, and evaluation data to avoid leakage. The Australian Government AI Technical Standard discusses this separation and synthetic data as a way to supplement dataset completeness (standard statement 19).
ISTQB’s sample answer notes that generated values could match real sensitive data; it does not provide an empirical probability for that risk (ISTQB sample answer v1.1). Treat potential matches as a reason to evaluate, not as a measured likelihood.
Choose an approach by fit, not by the word “AI”
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- Input basis: Is the method prompt-only, based on captured patterns, or shaped from source tables?
- Output: Do you need individual values, complete rows, or reusable generator code?
- Structure: Can it maintain schema, cross-field rules, referential integrity, and consistent keys?
- Privacy controls: What information is sent, how are outputs screened, and who can access them?
- Repeatability and integration: Can it regenerate stable fixtures and run within your current test pipeline?
- Operational fit: Does it require a particular environment, service, edition, data volume, or support process?
Enterprise services can also combine data masking, test-data mining, provisioning, synthetic generation, and database virtualization. Infosys describes such a test-data-management service (Infosys). IRI describes RowGen for referentially correct test data in production-like formats, but the cited page does not substantiate a generative-AI feature (IRI). These vendor descriptions establish marketed options, not independent comparative validation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Troubleshoot common failures
The response is not valid JSON
Ask for JSON only, define the exact fields and types, and parse the result automatically. If it still fails, reject and regenerate or use code to produce the output; do not silently repair malformed data in a way that changes test meaning.
Records look plausible but violate business rules
Add the invariant explicitly to the prompt or generator, then encode it as a validation assertion. A language model may satisfy the requested format while missing domain-specific logic.
Related rows do not join cleanly
Generate shared keys once and reference them across tables, or use a workflow that supports join-key consistency. Validate foreign keys after generation rather than assuming matching records were coordinated.
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Edge cases are missing
Request scenarios by name and count, then assert their presence. “Realistic” generation tends to describe ordinary-looking values unless the required boundary and invalid cases are explicit.
Output resembles sensitive source data
Stop before deployment or sharing. Review whether sensitive source material was supplied, restrict access to the output, and assess re-identification and similarity risk under your organization’s controls. A synthetic label or optional similarity filter is not proof of anonymity.
Snowflake synthesis fails with the similarity filter enabled
Check for nulls in non-string columns; Snowflake documents these as causing failure when the filter is enabled. Review the procedure’s input requirements and configuration in the procedure reference.
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Frequently Asked Questions
Does synthetic test data count as anonymised data?
Not automatically. Whether a dataset is identifiable depends on its inputs, outputs, and the possibility of linking it with other information; assess it under the applicable privacy and security controls.
Is generative AI required to create useful test data?
No. A deterministic generator or faker-backed program may be a better fit when the schema and rules are known and stable; generative AI is one way to draft values or generator code.
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