Skip to main content

​
1. Prepare Your Dataset

Create a JSON payload with:
  • Seed prompt: Your initial prompt template
  • Dataset: Examples with inputs and expected outputs (size must equal minibatchSize + paretoSize)
  • Evaluator: Must use available models
  • Budget: Number of inference calls (minimum 10, recommended 25-50)
  • Model: Must use available models
Each dataset item needs:
  • input: Object with variables matching your datasetColumns
  • expectedOutput: The desired response (minimum 1 character)
Important: Dataset size must equal minibatchSize + paretoSize. The first minibatchSize items are used for training (feedback), and the next paretoSize items are used for validation (pareto). For example, if minibatchSize: 3 and paretoSize: 4, you need exactly 7 dataset items in order.

​
2. Submit Execution

POST your payload to start evolution. The API returns an evolutionId immediately.

​
3. Monitor Progress

Poll the execution status every 10-30 seconds until completion. Status values:
  • pending: Job queued
  • in_progress: Actively evolving
  • completed: Finished successfully
  • failed: Encountered an error

​
4. Retrieve Results

Once completed, fetch the prompt evolution history to see how your prompt improved across generations.