> ## 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.

# Dataset Preparation

> Reviewing, fixing, and augmenting your training data

Preparation is where you turn raw data into a high-quality training set. The quality of your model is directly proportional to the quality of this data.

### Data Hygiene

* **Avoid Overlap**: Ensure your Fine-tune Dataset does not overlap with your **Test Set**. You want to train on one set of data and validate on a completely different set to ensure the model generalizes well.
* **Curate for Quality**: Remove incorrect, ambiguous, or low-value examples.

### AI Review & Modification

* **AI Review**: Use high-capability models to systematically check your dataset for accuracy and consistency.
* **Broad Modification**: Perform bulk updates using **Regex Keyword Replacement** to instantly align thousands of responses with new requirements or brand guidelines across your entire dataset.

### Augmentations

* **Reasoning Augmentation**: Teach your model to think step-by-step by attaching structured chain-of-thought reasoning to each training example. [Learn more](/build/models/reasoning_augmentation)
* **Generate from Examples**: Use your best examples as seeds to generate dozens of similar but unique training cases.
* **Variation Injection**: Automatically create variations in tone and phrasing to make your model more robust to different user styles.

## Next

* Add structured reasoning to your data: [Reasoning Augmentation](/build/models/reasoning_augmentation)
* Combine datasets into a training recipe: [Compositions](/build/models/compositions)
* Full end-to-end walkthrough: [Fine-tune a Model](/examples/fine_tuning)