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Your dataset library holds the data you test models on. Upload data from a file, Seekr, or HuggingFace, then map its columns so evaluators and examinations know what to send a model and how to score the response. Each row supplies a user question, and optionally a system prompt, retrieved context, prior conversation, and an expected answer. You can build your dataset library before adding any models or evaluators.

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How a dataset is structured

When you create a dataset, you map your source columns onto SeekrGuard’s message templates. These are the fields evaluators look for: Which fields a dataset contains determines which evaluators it can run. For example, an evaluator that requires an expected answer can only run on a dataset that maps Assistant Message. See Run examinations.

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Add a dataset

1

Add Dataset

On the Datasets page, click Add Dataset.
2

Upload Dataset

Enter a Name and Description, then choose a tab under Data Source.
Drag and drop a CSV or Parquet file onto the upload area, or click the area to choose a file from your computer.
Click Next.
3

Configure Templates

Your source columns appear as chips under Available Columns. Drag them onto the message templates.
  • To add placeholders by typing instead of dragging, click Edit on a message template.
  • A message template is a pattern applied to every row. A placeholder, written {{{column_name}}}, marks where that column’s value goes when the row is sent to a model.
  • The Live Preview panel shows the messages a row will send to the model. Switch rows with the Example list.
Click Next.
4

Preview & Save Dataset

Review the Dataset Preview, Template Configuration, and Sample Data Preview, then click Save Dataset. Any template you left unmapped reads not set in Template Configuration.
SeekrGuard extracts tool_definitions and expected_tool_calls columns as tool metadata. You do not map them to a message template. The tool evaluators, such as Tool Name Match and Tool Result Coverage, use them to score a model’s tool calls.

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Manage your datasets

The Datasets list shows one row per dataset, with Name, Description, Num. Input Samples, Num. Output Samples, Created By, Last Updated, Visibility, and Actions. Each dataset’s Visibility appears as a chip: private, public, or collaborative. See Visibility. Above the table, filter by Name, reorder the list with Sort By, and limit it to your own datasets with Only mine. Each row’s Actions column offers:
  • View Dataset – open the dataset’s detail page to inspect its inputs and outputs.
  • Delete Dataset – available only if you own the dataset or you are an admin. The dataset is archived rather than permanently destroyed.
Last modified on August 10, 2026