How to Generate Custom Synthetic Datasets using AI Prompts

Synthetic data generation is rapidly replacing manually created mock databases. Using generative AI prompts allows data teams to describe complex schemas, distributions, and relationship logic in plain English, and receive consistent tabular datasets. This guide covers best prompting practices.

Why use AI-driven Synthetic Data?

Standard mock libraries (like Faker) populate fields with random values but fail to maintain logical coherence across columns. For example, a birthdate column might not align with a career history start date. AI-driven generator engines analyze prompts to produce relationally consistent columns.

Prompt design tips for dataset generation

When drafting prompts to compile datasets, use the following structured template details:

• Role & Context: Explain who you are building data for (e.g. testing an e-commerce cart).
• Entity Definitions: List all necessary columns explicitly.
• Constraints & Rules: Define specific boundary values (e.g. price must be positive, active users must have login timestamps).
• Rows count: Specify output size format (e.g. 50 user rows, 100 transaction records).

Example dataset generation prompt

Here is a professional data engineers prompt designed to populate a payment database:

Generate a JSON array of 50 transaction items matching these rules:
- transaction_id: UUID format.
- customer_name: Full name.
- purchase_date: Random timestamps between March 2026 and May 2026.
- amount: Float range between 10.00 and 500.00.
- payment_status: 80% probability 'Success', 15% 'Failed', 5% 'Pending'.
- refund_reason: Must be null unless payment_status is 'Failed', in which case select from ['insufficient_funds', 'network_timeout', 'expired_card'].

How to parse AI synthetic responses in JavaScript

Once you retrieve the synthetic dataset from the LLM endpoint, you can parse it client-side into JSON or convert it to tabular arrays:

const aiResponse = `[
  {"transaction_id": "8b5a7", "customer_name": "Sarah", "amount": 42.50, "status": "Success"},
  {"transaction_id": "3e91c", "customer_name": "Leo", "amount": 120.00, "status": "Failed"}
]`;

try {
  const transactions = JSON.parse(aiResponse);
  console.log(`Parsed ${transactions.length} rows successfully.`);
  
  // Calculate average transaction amount
  const total = transactions.reduce((acc, t) => acc + t.amount, 0);
  console.log(`Average amount: $${(total / transactions.length).toFixed(2)}`);
} catch (error) {
  console.error("Failed to parse response:", error);
}

Generate datasets instantly using Sylvaera

If you don't want to integrate custom API scripts, you can use Sylvaera's Prompt to Dataset tool. Simply type your prompt description, click generate, and download your dataset as CSV, JSON, or SQL inserts in seconds.