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

# Epiq AI terminology

## Epiq AI terminology

The table below outlines some terms that you may come across as you use Epiq AI.

| Term                | Description                                                                                                                                                                                     |
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Review protocol     | A Word or PDF document that contains a list of review tags (e.g., Responsive, Privileged) and their definitions.                                                                                |
| Search protocol     | An Excel or CSV file that contains a list of search terms in dtSearch format.                                                                                                                   |
| Review batch        | A list of documents for review. The batch can be static (with a fixed set of documents) or dynamic (documents are sampled continuously from active learning).                                   |
| Review assignment   | A document in a review batch that is assigned to a reviewer or a group of reviewers.                                                                                                            |
| Dataset             | A subset of the document corpus filtered by search criterion. A dataset can be used in active learning, model training, evaluations, and exports.                                               |
| Model               | A machine learning model that can be used to predict how likely a document is relevant to a category or tag.                                                                                    |
| Instruction         | A sentence or a paragraph that describes the criteria for assigning a document with a certain category or tag.                                                                                  |
| Predictions         | The outputs of a machine learning model, including a prediction score for each document. The scores range from 0 to 1, with 1 indicating the most relevant and 0 indicating the least relevant. |
| Metrics             | Evaluation metrics for measuring the accuracy of a machine learning model. It also monitors the prediction statistics, e.g., what percentage of documents is predicted as relevant.             |
| Annotations         | The outputs of reviewed documents, including tags, predicted tags, comments, highlights, etc.                                                                                                   |
| Tags                | Tags assigned by human reviewers. They are defined in the tag settings.                                                                                                                         |
| Predicted Tags      | Tags predicted by machine learning models.                                                                                                                                                      |
| Classification task | The task of assigning tag(s) to a document as a whole.                                                                                                                                          |
| Extraction task     | The task of annotating parts of the documents, e.g. a text span or a region.                                                                                                                    |

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