Q3 of 21 · AI for testing
How do you use AI to generate realistic test data?
Short answer
Short answer: Describe the schema and constraints to a language model and ask it to generate sample records — names, addresses, realistic numeric ranges, and edge-case values. For large volumes or PII-safe synthetic data, use a purpose-built tool rather than a general-purpose LLM.
Detail
LLMs are good at generating small, semantically coherent datasets: product reviews with varied sentiment, user profiles with realistic names and locales, or a mix of valid and invalid postal codes for boundary testing. This is faster than maintaining a seed script for simple cases.
For production-scale synthetic data or data that must respect privacy constraints, general-purpose models are not the right tool. Purpose-built synthetic data libraries (Faker, Synth, Tonic.ai) give deterministic control over distribution and volume that a model cannot reliably guarantee.
AI-generated test data also requires review: a model might produce data that passes your validation schema but is semantically wrong — a "valid" UK postcode for an address in Tokyo, or a card number that passes the Luhn check but has an impossible BIN. See Synthetic test data with LLMs and PII-safe test data.
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