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Using a Synthetic Conversation Tree (SCT), Maitai can “walk” the tree and generate thousands of unique, valid conversation paths automatically.

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Why use Synthetic Data?

  1. Bootstrap New Applications: Get a high-quality model before you have a single real user interaction.
  2. Cover Edge Cases: Generate examples for rare or dangerous scenarios that you don’t want to wait for in production.
  3. Balance your Data: If your production data is heavily skewed (e.g., 90% simple questions, 10% complex), you can use synthetic data to ensure your model is well-trained on complex cases.

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Workflow: From Tree to Fine-tuning

  1. Define the Tree: Map out the core logic of your intent or agent in the visualizer.
  2. Generate Samples: Use Maitai to generate a set of conversations based on the tree logic.
  3. Review & Refine: Inspect the generated conversations to ensure they meet your quality standards.
  4. Create Dataset: Export the generated paths into a Fine-tune Dataset or a Test Set.
  5. Fine-tune: Train your model on this robust, structured data.

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