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

# How to run evals?

> Future AGI's Eval tasks allows you to create and run automated tasks on your data. These tasks enable **automated workflows** to manage model **evaluation** at scale. They provide ways to operationalize evaluations and track ongoing results without requiring manual intervention. Users can create and run automated tasks on their data.

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## Step-by-Step Guide to Creating an Eval Task

### 1. Set Filters Based on Span Kind

Begin by defining a set of filters to narrow down the data you want to evaluate. Filters can be based on various properties such as:

* Node Type
* Created At

These filters help you target specific datasets for evaluation.

### 2. Choose Data Type

Decide whether you want to run the Evals on:

* **Historic Data**: Apply Evals to a specified time range of already-collected data.
* **Continuous Data**: Run the evaluation automatically as new data arrives. Recommended for continuous monitoring data in a production environment.

### 3. Define Sampling Rate

Set a **sampling rate** to determine the percentage of data to process. A sampling rate of (100%) means all data items are used, whereas (50%) means only half of the available data is used for evaluation. This helps control **costs** and manage **data volume**.

### 4. Set Maximum Number of Spans

Define the maximum number of spans for each evaluation run. This ensures your evaluation scales well and avoids processing excessive amounts of data at once.

### 5. Select Evals to Run

Choose from a list of **preset** or **previously configured evaluations (Evals)** that you want to apply to your filtered data. This selection determines which evaluations will be executed.

For example, if you want to perform a **Bias Detection** evaluation, each evaluation requires specific keys.

In the case of Bias Detection, an input key is essential. Every [span](/future-agi/products/observability/concept/spans) contains key-value pairs, known as [span attributes](/future-agi/products/observability/concept/spans#span-attributes), where the data is stored. You need to supply one of these span attributes as the input. For instance, by passing `llm.output_messages.0.message.content` as the input, the Bias Detection evaluation will determine whether the content is biased. The evaluation will return `Passed` if the content is neutral, or `Failed` if any bias is detected.

For more information on the evaluations we support, please refer to the [evals documentation](/future-agi/get-started/evaluation/builtin-evals/overview).

### 6. Run the Task

Once all configurations are set, run the task. You can test the configuration to verify that the Evals and filters are correct before saving the task.

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