# Create New Dataset

**Steps to Create a Dataset**

1. **Navigate to the Dataset Page**
   * After creating a new project, you will be automatically redirected to the dataset page.
2. **Start with an Empty Screen**
   * If no datasets exist, you will see a guided instruction screen prompting you to create your first dataset.
3. **Create a Dataset Using Tracing**
   * Datasets in agentic applications are created by committing a trace.
   * Refer to the [**Tracing Documentation**](/ragaai-catalyst/agentic-testing/concepts/tracing.md) to learn how to set up and capture traces.
4. **Commit Your Trace**
   * Once a trace is successfully recorded and committed, it will automatically populate the dataset list on the UI.
   * Each dataset represents a specific experiment conducted within the application.
5. **View the Dataset**
   * After committing a trace, the dataset will appear in the UI, allowing you to access and manage it as an experiment.

***

**Dataset Columns:**

1. **TraceID**
2. **Timestamp**
3. **Trace URL**
4. **Feedback**
5. **Response**
6. **Metadata**
7. **Metric Score**
8. **Completion Token**
9. **Prompt Token**
10. **Cost**

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#### **Trace View and Prediction Details**

When you click on a datapoint in the dataset table, the **Trace View** opens, providing detailed insights at the span level:

**Trace View**

* Displays all the information instrumented during tracing, broken down at the span level.
* Offers a granular view of the execution flow, including:
  * LLM calls.
  * Tool interactions.
  * Agent decision-making steps.
* Enables debugging and performance analysis by identifying bottlenecks or errors.

**Prediction Details**

* Users can toggle to the **Prediction Details** tab to focus on the evaluation metrics.
* Key features include:
  * **Metric Evaluation Score**: A breakdown of scores based on pre-defined metrics such as Response Correctness, Context Relevancy, etc.
  * **Reasoning**: Detailed explanations behind each score, offering insights into why a particular rating was assigned.

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