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

# Run examinations

> Run a batch evaluation and read the results.

Run a dataset through one or more models, score the outputs with one or more evaluators, and review the results.

## Before you start

You need:

* A [dataset](/guard/app/datasets).
* An [evaluator](/guard/app/evaluators) that dataset can support.
* A [model](/guard/app/models) to generate the outputs that evaluators score against the dataset.

## Create an examination

<Steps>
  <Step title="Create Examination">
    On the **Examinations** page, click **Create Examination**.
  </Step>

  <Step title="Configure Examination">
    1. Enter a **Name**. It is required.

    2. Add a **Description** with notes on the run's scope and how to interpret it.

    3. Choose a **Dataset**. The list shows each dataset's sample count.

    4. Set **Sample Selection** as a percentage of the dataset or a number of samples. The control shows how many of the total samples will be used.

    5. Choose **Generation Models** to produce the outputs to score. These are the models you want to evaluate for your use case.

    6. Check the evaluators you want in the **Evaluators** table, which lists the ones your dataset can support.

       * The four columns show the inputs each evaluator needs. A dash means it does not need that input, and a red ✕ means the dataset does not supply one it needs, which disables the evaluator.
       * Choosing a generation model supplies **MO**, which can enable more evaluators.
       * For each LLM-as-a-Judge evaluator, expand its row and choose one or more **Evaluator Models** to judge with. It cannot run without at least one.

       | Column | Input                                   |
       | ------ | --------------------------------------- |
       | UI     | User input, the user's question         |
       | CI     | Context input, retrieved material       |
       | MO     | Model output, the response being scored |
       | EO     | Expected output, the reference answer   |

    7. Click **Next**.
  </Step>

  <Step title="Review & Submit">
    Check the **Name**, **Description**, **Dataset**, **Sample Selection**, and the evaluators you picked, then click **Create Examination**.
  </Step>
</Steps>

## Manage your examinations

The **Examinations** list shows one row per run, with **Name**, **Description**, **Models**, **Datasets**, **Status**, **Visibility**, **Created**, and **Actions**.

Above the table, filter by **Name** or **Status**, reorder the list with **Sort By**, and limit it to your own runs with **Only mine**.

Each row's **Actions** column opens the examination, and offers a delete action for runs you created.

## Track an examination's progress

An examination is **queued** while it waits to run, **in progress** while it runs, then **completed** or **failed**.

If it fails, check that the models you selected are active, that the dataset supplies the fields your evaluators need, and that each LLM-as-a-Judge evaluator has a judge model. Then create the examination again.

## Read the results

Open a completed examination. Its header reports the models tested, the evaluators used, and the total number of evaluations, and the results are split across four tabs.

### Model Performance

A bar chart of each model's score, one evaluator at a time, chosen with **Filter by Evaluator**.

### Sample Responses

One row per input, with a count of the unique inputs scored. Expand a row to see the responses for that input and how many models and judges scored it.

### Interactive Analysis

Choose a **Chart Type**: scatter across two evaluators, a heatmap of model against evaluator, a box plot of each model's distribution, parallel coordinates, or a judge agreement matrix. **Filter Models** narrows any of them to specific models.

### Agentic Assessment

A cross-evaluator summary for runs that involve tools, with an **Overview** sub-tab and one sub-tab per agent-relevant evaluator.

**Overview** opens with **Exam Agentic Facts**: how many inputs carried tool definitions, how many carried expected tool calls, how many outputs invoked a tool, the number of distinct tools, and the tool names with their call counts. Below it, a composite score per model and a table of each evaluator's score alongside that composite.

Each evaluator's sub-tab shows its rubric, a per-model chart, and a table of scores by judge model.