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

# Datasets Quick Start

> Create a reproducible dataset from project traffic and inspect its training and test rows.

This quick start shows you how to build a dataset from the traces a project has already recorded. The result is a set of training and test rows for relabeling, fine-tuning, and evaluations.

<Note>
  Before you begin, send representative traffic through the project. Studio builds datasets from the project's own traces. To build a dataset from W\&B Weave calls logged outside the project, use the API source described in [Datasets](/model-distillation/studio/datasets#choose-a-source).
</Note>

<Steps>
  <Step title="Open the project">
    Select the project you want to improve, open **Datasets**, and select **New Dataset**.
  </Step>

  <Step title="Name the dataset">
    In **Dataset name**, keep the generated name or enter your own. The dataset is a self-contained snapshot of traces logged to this project in W\&B Weave.
  </Step>

  <Step title="Choose the source data">
    Under **Source data**, configure the following fields:

    * **Project version:** The version whose prompt and routing regime produced the traces you want. Studio selects the latest version by default.
    * **Served by:** The model whose responses become the training targets. To keep the training data teacher-only, select the teacher model. **All models** mixes the outputs of every model that served the version, which includes any fine-tuned student.
    * **After (UTC)** and **Before (UTC):** The fixed time window to select traces from. Studio prefills the last 14 days as fixed timestamps.
    * **Maximum traces:** The random sample size after filtering. The default is 20,000 and the maximum is 100,000. Each selected trace becomes one dataset entry.
  </Step>

  <Step title="Set the train and test split">
    To choose how many rows to reserve for evaluations, use the **Train / test split** slider. The default is 90% training and 10% test. Each complete trace stays in one split, so evaluations don't include near-duplicates from training.
  </Step>

  <Step title="Create and inspect">
    Select **Create Dataset** and wait for the status to become **Ready**. Review several training and test rows, check message and output fidelity, and delete unusable entries before training.
  </Step>
</Steps>

The dataset is now ready for [relabeling](/model-distillation/studio/relabeling), [fine-tuning](/model-distillation/studio/fine-tuning-quickstart), and [evaluations](/model-distillation/studio/evaluations-quickstart).

<Accordion title="API: Create a dataset (POST /tasks/{alias}/datasets)">
  Use fixed timestamps so rerunning your workflow describes the same source window. The `filters` object corresponds to the **Source data** fields in Studio: `task_version` is the project version, and `resolved_provider` with `resolved_model` is the **Served by** model. To include all models, omit those two keys:

  ```bash theme={"system"}
  curl --request POST \
    --url "https://distillation.training.wandb.ai/v1/tasks/ticket-classifier/datasets" \
    --header "Authorization: Bearer $WANDB_API_KEY" \
    --header "Wandb-Entity: your-team" \
    --header "Content-Type: application/json" \
    --data '{
      "name": "recent-production",
      "params": {
        "query": {
          "source": "task",
          "after": "2026-08-01T00:00:00.000Z",
          "before": "2026-09-01T00:00:00.000Z",
          "filters": {
            "task_version": 1,
            "resolved_provider": "openai",
            "resolved_model": "gpt-5.6-sol"
          }
        },
        "sampling": {"strategy": "random", "limit": 20000},
        "split": {"val": 0.1, "by": "trace"}
      }
    }'
  ```

  The response contains the dataset ID. Poll the dataset resource until `status` is `ready`. For details, see [Create a dataset](/model-distillation/reference/management/datasets/create-a-dataset).
</Accordion>