Generating Synthetic Test Data with LLMs (JSON Output)

June 18, 2026·8 min read·AI & JSON

Why generate synthetic data

Real production data is risky to use in tests — it contains PII, it is often unavailable in dev, and it rarely covers the edge cases you most need to test. LLMs are excellent at producing realistic, varied, schema-valid JSON on demand, giving you safe fixtures, demo content, and training examples without ever touching real user records.

Generate against a schema

The reliable pattern is to give the model your JSON Schema (or an example) and have it return matching records via structured output:

json

{
  "type": "array",
  "items": {
    "type": "object",
    "properties": {
      "name":   { "type": "string" },
      "email":  { "type": "string", "format": "email" },
      "plan":   { "type": "string", "enum": ["free", "pro", "enterprise"] },
      "signup": { "type": "string", "format": "date" }
    },
    "required": ["name", "email", "plan"]
  }
}

A prompt like "Generate 10 diverse user records matching this schema" returns ready-to-use fixtures, and because generation is schema-constrained, every record parses and validates.

Prompt for diversity, not repetition

Models left alone produce samey data — every user named "John" in "New York." Steer for variety:

text

Generate 20 user records matching the schema.
Make them diverse: varied names across cultures, a realistic mix of plans
(mostly free, some pro, few enterprise), signup dates spread across 2025-2026,
and include 2 edge cases (very long name, plus-addressed email).

Asking explicitly for edge cases is where LLM-generated data beats hand-written fixtures — it surfaces inputs you would not think to write.

Use cases

Test fixtures — populate unit and integration tests with valid, varied records.
Demo and seed data — fill a staging app with believable content.
Load testing — generate large volumes that match real shape.
Labeled examples — produce input/output pairs for evals or fine-tuning.

Validate what you generate

Synthetic data is only useful if it is correct. Always validate generated records against your schema before using them, and spot-check for realism. A quick pipeline:

text

1. Generate records via structured output.
2. Validate each against the JSON Schema.
3. Drop or regenerate any that fail.
4. De-duplicate and check distributions (no 90% "enterprise").

Cautions

Not a substitute for real-world testing. Synthetic data reflects the model's assumptions; keep some real (anonymized) samples for final validation.
Watch for bias. Models can over-represent common names or patterns; prompt for and verify diversity.
Keep it clearly synthetic. Never let generated data masquerade as real in analytics or training without labeling it.

Frequently asked questions

Use the provider's structured-output / JSON Schema mode and pass your schema, then validate each record. This guarantees parseable, correctly-shaped data.

Prompt explicitly for variety — varied names, realistic distributions, date ranges — and ask for specific edge cases. Then check the distributions before using the data.

For development and testing, yes — it carries no real PII. But validate it and keep some anonymized real samples for final checks, since synthetic data reflects the model's assumptions.

Try Random JSON Generator

Generate realistic mock JSON data with custom fields.

Open Random JSON →Schema Validator →