# Dataset Curation

**After** [**evaluation**](/user-interface/project/menu-bar/evaluate-a-model.md) **has completed**, you can initiate 3 different curation process:

1. [Unlabeled Data Recommendations ](/getting-value-from-tensorleap/active-learning.md)- Tensorleap analyzes your model's performance and data distribution to identify high-impact unlabeled samples. By focusing your labeling effort on the most informative samples, you can significantly accelerate model improvement and reduce labeling costs.
2. [Synthetic Data Optimization](/getting-value-from-tensorleap/synthetic-data-optimization.md) - Tensorleap aligns your synthetic data distribution with the target data source. By optimizing synthetic data generation, you can accelerate model improvement while significantly reducing labeling costs.
3. [Pruning](/getting-value-from-tensorleap/pruning.md) - Tensorleap analyzes your dataset distribution and rebalances it using your selected metadata tags.\
   Apply filters to focus on a subset and optionally prioritize specific metadata dimensions to guide the pruning process.

<figure><img src="/files/qcZ0dHf1wnZgfr5xJ5gl" alt=""><figcaption></figcaption></figure>


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.tensorleap.ai/user-interface/project/menu-bar/dataset-curation.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.