> ## Documentation Index
> Fetch the complete documentation index at: https://docs.trymaitai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Data Generation

> Creating robust synthetic datasets for training and testing

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

### 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.

### 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.

## Next

* Create a dataset from traffic or synthetic data: [Dataset Creation](/build/models/dataset_creation)
* Full end-to-end walkthrough: [Fine-tune a Model](/examples/fine_tuning)