> For the complete documentation index, see [llms.txt](https://docs.tensorleap.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tensorleap.ai/user-interface/project/menu-bar/dataset-curation.md).

# Dataset Curation

Learn how to get labeling recommendations using Tensorleap's Dataset Curation functionality.

**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="https://3509361326-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F9UXeOlFqlw8pl79U2HGU%2Fuploads%2FaW8I3hql27bIYszHswMb%2FScreenshot%202026-05-03%20at%2013.55.28.png?alt=media&amp;token=30fe597b-009d-42f7-9c48-55ea3eb7881c" alt=""><figcaption></figcaption></figure>


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