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

# Running evaluators

> Learn the common workflow for running any evaluator: choose content, run the evaluator, and review results.

All evaluators follow this workflow, even though their inputs and outputs may differ. For evaluator-specific setup, inputs, prompts, and interpretation, see the documentation for the evaluator you want to run.

## What you need

Before you begin, make sure you have:

* API key from the model provider.
* Python workspace, the [Evaluators Playground](https://platform.learningcommons.org) ↗, or the appropriate [SDK](/evaluators/sdks/overview).
* The text you want to evaluate.
* Required context (if applicable): Inputs such as grade level or intended audience.

## What you'll do

### STEP 1: Choose the content to evaluate

1. Select the content you want to evaluate.
   Make sure the content:
   * Matches the evaluator's intended content type (e.g., informational text or conversation output)
   * Falls within documented length and format constraints
   * Does not include personal or sensitive data
2. Prepare the inputs required by the evaluator. Refer to the evaluator's page for this info. This usually includes:
   * The content to evaluate

* Any required contextual parameters, like the intended grade level.

### STEP 2: Run the evaluator

1. Run the evaluator using the:
   * Provided prompts
   * Recommended  LLM model
     mentioned in the evaluator's documentation page.

<Note>
  We recommend running the prompt 3 times and aggregating the results using a simple majority rule to improve accuracy when creating or validating your prompts, and running once when you are building our prompt into your production code.
</Note>

### STEP 3: Review the results

1. Review the evaluator output based on the interpretation guidelines on the evaluator's documentation page.