---
title: Create and manage Relevance Generative Answering (RGA) models
slug: nb6a0085
canonical_url: https://docs.coveo.com/en/nb6a0085/
collection: leverage-machine-learning
source_format: adoc
---
# Create and manage Relevance Generative Answering (RGA) models

> **Important**
>
> Coveo Relevance Generative Answering (RGA) is a paid product extension.
> Contact [Coveo Sales](https://www.coveo.com/en/contact) or your Account Manager to add RGA to your [organization](https://docs.coveo.com/en/185/) license.

[Relevance Generative Answering (RGA)](https://docs.coveo.com/en/n9de0370/) leverages [generative AI](https://docs.coveo.com/en/n9e90153/) technology to generate answers to complex natural language user queries in a Coveo-powered [search interface](https://docs.coveo.com/en/2741/).

To implement RGA, first create an [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/).

## What does an RGA model do?

When an [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/) builds, it creates [embeddings](https://docs.coveo.com/en/n9de0370#embeddings) for the indexed items specified in the model settings and stores the embeddings in model memory.

> **Note**
>
> The model is preconfigured to rebuild and update the embeddings weekly based on when the model is created.
> Contact your Coveo Account Manager if a different build interval is required.

> **Tip**
>
> By default, [RGA](https://docs.coveo.com/en/nbtb6010/) supports content retrieval and answer generation only in English.
> However, Coveo offers beta support for content retrieval and answer generation in languages other than English.
> Learn more about [multilingual content retrieval and answer generation](https://docs.coveo.com/en/p5ne0024/).

![Relevance Generative Answering passage embedding | Coveo](https://docs.coveo.com/en/assets/images/leverage-machine-learning/rga-embed-passage-level.png)

An [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/) uses a pre-trained sentence transformer language model to create the embeddings.
The language model does this by capturing relationships between words, phrases, and sentences in the dataset.

An [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/) creates [embeddings](https://docs.coveo.com/en/ncc87383/) only for the content in an [item](https://docs.coveo.com/en/210/)'s body, which is the content mapped to the `body` field in the Coveo index.
For more information, see [How RGA uses your content](https://docs.coveo.com/en/nb6a0008#how-rga-uses-your-content).

> **Notes**
>
> * The [CPR](https://docs.coveo.com/en/oaie9196/) [model](https://docs.coveo.com/en/1012/) uses only the content in an item's `body` field.
> Content in other searchable fields, such as `title`, `author`, `source`, and `date`, isn't embedded by the [model](https://docs.coveo.com/en/1012/) and therefore isn't considered during passage retrieval.
> Passages are retrieved solely based on the semantic similarity between the query and the content of an item's body.
> For example, even if a query matches terms in an item's `title` field, the [CPR](https://docs.coveo.com/en/oaie9196/) model won't retrieve passages from that item unless the body content is semantically relevant.
> 
> * RGA uses fields such as `title`, `uri`, and `clickableuri` to populate the [citations in the RGA component](https://docs.coveo.com/en/nb6a0037#rga-component-features).
> If a field has the [**Multi-value facet** option enabled](https://docs.coveo.com/en/1833#facet-and-multi-value-facet), RGA uses only the first value of the field for the citations.

As shown in the following diagram, the model uses [chunks](https://docs.coveo.com/en/n9de0370#chunking) to create the [embeddings](https://docs.coveo.com/en/ncc87383/).
Instead of creating a vector for each individual word, a vector is created for a segment of text (chunk) to increase relevance.

![Vector space | Coveo](https://docs.coveo.com/en/assets/images/leverage-machine-learning/rga-chunk-example.png)

As shown in the following diagram, when a user enters a query in a Coveo-powered search interface, the [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/) embeds the query in the embedding vector space in its memory to find the most relevant segments of text ([chunks](https://docs.coveo.com/en/n9de0370#chunking)).
In the context of the RGA answer-generation flow, this is referred to as [second-stage content retrieval](https://docs.coveo.com/en/n9de0370#second-stage-content-retrieval).
Only the chunks corresponding to the most relevant items identified during [first-stage content retrieval](https://docs.coveo.com/en/n9de0370#first-stage-content-retrieval) (items retrieved by the Coveo search engine) are considered.
The most relevant chunks are then used to [generate an answer](https://docs.coveo.com/en/n9de0370#answer-generation).

![Vector space query | Coveo](https://docs.coveo.com/en/assets/images/leverage-machine-learning/grouped-rga.png)

> **Tip**
>
> The embeddings that are created by the [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/) aren't impacted by [Coveo Analytics events](https://docs.coveo.com/en/260/).
> However, an [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/) leverages the most relevant items retrieved by your Coveo-powered search for a given user query.
> Therefore, by enabling an [Automatic Relevance Tuning (ART)](https://docs.coveo.com/en/1013/) [model](https://docs.coveo.com/en/1012/), which learns from [events](https://docs.coveo.com/en/260/), then the most relevant items, and by extension the generated answer, will be influenced by events.

> **Note**
>
> You can set a custom value for the maximum number of items that the RGA model considers when retrieving the most relevant segments of text (chunks).
> You can also modify the relevancy threshold that's used by the [RGA](https://docs.coveo.com/en/nbtb6010/) model to determine if a chunk is relevant enough to be considered by the [RGA](https://docs.coveo.com/en/nbtb6010/) model for answer generation.
> 
> These are advanced [model](https://docs.coveo.com/en/1012/) query pipeline association configurations that should be used by experienced Coveo administrators only.
> For more information, see [RGA model association advanced configuration](https://docs.coveo.com/en/nb6a0104#rga-model-association-advanced-configuration).

## Prerequisites

* You have the [required privileges](#required-privileges) to create an [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/).

* The content you want to use for the model meets the [item requirements](https://docs.coveo.com/en/nb6a0008#requirements) and is [optimized](https://docs.coveo.com/en/nb6a0008#best-practices) for RGA.

> **Note**
>
> An RGA implementation should include both an [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/) and a [Semantic Encoder (SE)](https://docs.coveo.com/en/nbtb0041/) [model](https://docs.coveo.com/en/1012/).
> The same SE model can be used with multiple RGA models.
> Both the RGA and SE models must be configured to use the same content.
> 
> See [RGA overview](https://docs.coveo.com/en/n9de0370#rga-overview) for information on how RGA and SE work together in the context of a search session to generate answers.

> **Important**
>
> Keep the [model embedding limits](#model-embedding-limits) in mind when choosing the content for your model.

## Create an RGA model

. On the [**Models**](https://platform.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) ([platform-ca](https://platform-ca.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) | [platform-eu](https://platform-eu.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) | [platform-au](https://platform-au.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/)) page of the [Coveo Administration Console](https://docs.coveo.com/en/183/), click **Add model**, and then click the **Relevance Generative Answering** card.

. Click **Next**.

. In the **Learn from** section, select the content that the model will use.
You can select the sources and apply additional filters using the **Standard** configuration, or use **Advanced** mode to define a custom filter expression.

> **Important**
>
> You'll lose the current mode settings when you switch between **Standard** and **Advanced** mode.

> **Note**
>
> An RGA implementation should include both an [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/) and a [Semantic Encoder (SE)](https://docs.coveo.com/en/nbtb0041/) [model](https://docs.coveo.com/en/1012/).
> The same SE model can be used with multiple RGA models.
> Both the RGA and SE models must be configured to use the same content.
> 
> See [RGA overview](https://docs.coveo.com/en/n9de0370#rga-overview) for information on how RGA and SE work together in the context of a search session to generate answers.

> **Tip**
>
> The **Data volume preview** section shows the impact of your settings on the data that's available to the model.

** In the **Standard** tab:

.. In the **Sources** dropdown menu, select the [sources](https://docs.coveo.com/en/246/) that contain the [items](https://docs.coveo.com/en/210/) from which you want the model to learn.

--
> **Note**
>
> If your Coveo organization includes [multiple indexes](https://docs.coveo.com/en/2877/), the model can learn only from sources that are linked to the default index.
--

.. (Optional) In the **Apply filters on dataset** section, you can specify a condition to segment the content on which the model should base its training:

**Example**

You want the model to base its training only on items for which the **collection** [field](https://docs.coveo.com/en/200/) have the `FAQ` value.

Therefore, you add a `collection is equal to FAQ` condition.

... Click **Add filter(s)**.

... In the **Field name** input, enter the name of the [field](https://docs.coveo.com/en/200/) that you want to use to segment the dataset.

... In the **Select an operator** dropdown menu, select the desired operator.

... In the **Value** input, enter the value of the field on which you want to segment the dataset.

... Click **Apply**.

** In the **Advanced** tab:

.. Enter a custom filter expression using [Coveo query syntax](https://docs.coveo.com/en/1552/).

.. Click **Apply**.

. Click **Next**.

. In the **Name your model** input, enter a meaningful display name for the model.

. (Optional) Use the **Project** selector to associate your model with one or more [projects](https://docs.coveo.com/en/n7ef0517/).

. Click **Start building**.

. [Associate the model with a pipeline](https://docs.coveo.com/en/nb6a0104/) to use the [model](https://docs.coveo.com/en/1012/) in a search interface.

> **Tip**
>
> An effective [RGA](https://docs.coveo.com/en/nbtb6010/) implementation relies on a process of continuous improvement that includes evaluating the generated answers and modifying the implementation based on the evaluation results.
> 
> The [Coveo Knowledge Hub](https://docs.coveo.com/en/p58d0270/) provides the tools you need to evaluate and improve your [RGA](https://docs.coveo.com/en/nbtb6010/) implementation.

## Add a custom prompt instruction

A prompt is an input text that's given to a generative LLM to guide it in generating a specific output.
During the [answer generation process](https://docs.coveo.com/en/n9de0370#answer-generation), the [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/) automatically creates a prompt and sends it to the generative LLM.
The prompt includes the user query, an instruction, and the most relevant [chunks](https://docs.coveo.com/en/n9de0370#chunking) of text.
The [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/)'s base instruction directs the LLM to generate the answer using only the chunks provided in the prompt.
This [grounds](https://docs.coveo.com/en/nccf0415/) the LLM response to the most relevant information, which is an essential part of [retrieval-augmented generation (RAG)](https://docs.coveo.com/en/p8ie0159/).

However, you can add an additional instruction to better guide the generative LLM based on your use case.
For example, you can add a custom instruction to adjust the tone or style of the answer to better align with your company's voice and branding.
You can include a role-specific instruction to tailor the answer to specific roles or audiences, such as a sales agent or customer support representative.
You can also provide restrictions so that generated answers don't mention specific products or competitors, give financial advice, or show other sensitive information.

Your custom prompt instruction is added to the default base prompt instruction that the [RGA](https://docs.coveo.com/en/nbtb6010/) model generates automatically.
In other words, your custom instruction doesn't replace the base instruction but enhances it.

> **Important**
>
> Adding a custom prompt instruction affects all answers generated across [query pipelines](https://docs.coveo.com/en/180/) that use the [model](https://docs.coveo.com/en/1012/).
> While this can improve answers for certain [queries](https://docs.coveo.com/en/231/), it may also cause inaccuracies, inconsistencies, or unintended results.
> 
> You're responsible for configuring, testing, and maintaining your prompt instruction to ensure compatibility with the Coveo Platform and future updates.
> This includes providing role-specific guidance or exemplars for creating a prompt instruction, and monitoring outputs at scale to detect issues.
> The [Knowledge Hub](https://docs.coveo.com/en/p58d0270/) can help you evaluate and refine results.
> 
> Custom instructions aren't covered by Coveo's automated tests, which are limited to the default base prompt.
> Because the base prompt is updated frequently, you should retest your prompt instruction regularly.
> 
> Be aware of the following risks when adding a custom prompt instruction:
> 
> * Semantic overload may produce inaccurate or harmful responses.
> 
> * No Coveo tools are provided to validate answer quality at scale.
> 
> * A custom prompt instruction may introduce security risks, such as prompt injection.

To add a custom prompt instruction for your [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/)

> **Note**
>
> You can add a custom prompt instruction only by editing an existing [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/).
> You can't add an instruction when creating an [RGA](https://docs.coveo.com/en/nbtb6010/) model.

. On the [**Models**](https://platform.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) ([platform-ca](https://platform-ca.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) | [platform-eu](https://platform-eu.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) | [platform-au](https://platform-au.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/)) page, click the model you want to edit, and then click **Edit** in the Action bar.

. On the subpage that opens, select the **Configuration** tab.

. Under **Prompt enhancement**, enable **Prompt instruction**.

. Enter your custom prompt instruction.

> **Notes**
>
> * The following default template instruction appears in the text box when the feature is enabled.
> The template is a guide that you can use to create your own prompt instruction.
> 
> ```text
You are a subject matter expert representing [Enterprise Name] and must operate strictly within [Enterprise Domain]'s guidelines and applicable regulations. Your responses must reflect [Enterprise Name]'s values, tone, and content standards.
Do not offer personalized advice, opinions, or speculative commentary.
Use only factual, approved, or customer-provided content.
If a topic falls outside the approved scope, politely decline to answer or redirect to official resources.
Avoid referencing competitors, unverified tools, or external platforms unless explicitly allowed.
When uncertain, refrain from answering to avoid compliance or brand risks.
Maintain a [tone guidance — for example, neutral, respectful, brand-aligned, professional, inclusive] tone in all responses.
Never generate or assist with content that violates safety, legal, privacy, or ethical standards.
Always prioritize [compliance/safety/accuracy/customer trust] based on [Customer Name]'s core mission.
```
> 
> * [RGA](https://docs.coveo.com/en/nbtb6010/) doesn't support variables or function calls in the prompt instruction.
> 
> * The custom prompt instruction is limited to 2,000 characters.

. Click **Save**.

> **Note**
>
> Saving changes to the prompt instruction automatically triggers a [model](https://docs.coveo.com/en/1012/) rebuild.
> Your changes take effect only after the rebuild completes and the new [model](https://docs.coveo.com/en/1012/) version is live in your Coveo [organization](https://docs.coveo.com/en/185/).
> Depending on the amount of data processed by your model, it may take up to 1 hour for a [model](https://docs.coveo.com/en/1012/) to go live following a rebuild.

> **Important**
>
> Disabling the prompt instruction feature and then saving the [model](https://docs.coveo.com/en/1012/) deletes the current prompt instruction.
> To preserve your custom prompt instruction, make sure to copy it before disabling the feature.
> If the feature is re-enabled, the default template prompt instruction appears.

## Manage an RGA model

You can [edit](#edit-an-rga-model), [delete](#delete-an-rga-model), or [review information](#review-model-information) for your [model](https://docs.coveo.com/en/1012/).

### Edit an RGA model

. On the [**Models**](https://platform.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) ([platform-ca](https://platform-ca.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) | [platform-eu](https://platform-eu.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) | [platform-au](https://platform-au.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/)) page, click the model you want to edit, and then click **Edit** in the Action bar.

. On the subpage that opens, select the **Configuration** tab.

. Under **Name**, edit the model's display name.

. (Optional) Use the **Project** selector to associate your model with one or more [projects](https://docs.coveo.com/en/n7ef0517/).

. In the **Learn from** section, select the content that the model will use.
You can select the sources and apply additional filters using the **Standard** configuration or use **Advanced** mode to define a custom filter expression.

> **Important**
>
> You'll lose the current mode settings when you switch between **Standard** and **Advanced** mode.

> **Note**
>
> An RGA implementation should include both an [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/) and a [Semantic Encoder (SE)](https://docs.coveo.com/en/nbtb0041/) [model](https://docs.coveo.com/en/1012/).
> The same SE model can be used with multiple RGA models.
> Both the RGA and SE models must be configured to use the same content.
> 
> See [RGA overview](https://docs.coveo.com/en/n9de0370#rga-overview) for information on how RGA and SE work together in the context of a search session to generate answers.

> **Tip**
>
> The **Data volume preview** section shows the impact of your settings on the data that's available to the model.

** In the **Standard** tab:

.. In the **Sources** dropdown menu, select the [sources](https://docs.coveo.com/en/246/) that contain the [items](https://docs.coveo.com/en/210/) from which you want the model to learn.

--
> **Note**
>
> If your Coveo organization includes [multiple indexes](https://docs.coveo.com/en/2877/), the model can learn only from sources that are linked to the default index.
--

.. (Optional) In the **Apply filters on dataset** section, you can specify a condition to segment the content on which the model should base its training:

**Example**

You want the model to base its training only on items for which the **collection** [field](https://docs.coveo.com/en/200/) have the `FAQ` value.

Therefore, you add a `collection is equal to FAQ` condition.

... Click **Add filter(s)**.

... In the **Field name** input, enter the name of the [field](https://docs.coveo.com/en/200/) that you want to use to segment the dataset.

... In the **Select an operator** dropdown menu, select the desired operator.

... In the **Value** input, enter the value of the field on which you want to segment the dataset.

... Click **Apply**.

** In the **Advanced** tab:

.. Enter a custom filter expression using [Coveo query syntax](https://docs.coveo.com/en/1552/).

.. Click **Apply**.

. In the **Prompt enhancement** section, you can [add a custom prompt instruction](#add-a-custom-prompt-instruction) for the [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/).

. Click **Save**.

> **Note**
>
> Some configuration changes initiate an automatic model rebuild when you save the model.
> The [**Models**](https://platform.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) ([platform-ca](https://platform-ca.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) | [platform-eu](https://platform-eu.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) | [platform-au](https://platform-au.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/)) page shows your model's current **Status**.
> Model settings take effect only when its status is **Active**.
> 
> For more information on Coveo ML model statuses, see the [**Status** column reference](#status-column).

### Delete an RGA model

> **Note**
>
> If the model is associated with a query pipeline, make sure to dissociate the model from the query pipeline after deleting it.

. On the [**Models**](https://platform.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) ([platform-ca](https://platform-ca.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) | [platform-eu](https://platform-eu.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) | [platform-au](https://platform-au.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/)) page, click the ML model that you want to delete, and then click **More** > **Delete** in the Action bar.

. In the panel that appears, click **Delete**.

### Review model information

On the [**Models**](https://platform.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) ([platform-ca](https://platform-ca.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) | [platform-eu](https://platform-eu.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) | [platform-au](https://platform-au.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/)) page, click the desired model, and then click **View** in the Action bar.
For more information, see [Reviewing model information](https://docs.coveo.com/en/1894/).

## Model embedding limits

The [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/) converts your content's body text into numerical representations ([vectors](https://docs.coveo.com/en/nccf9008/)) in a process called [embedding](https://docs.coveo.com/en/ncc87383/).
It does this by breaking the text up into smaller segments called chunks, and each chunk is mapped as a distinct vector.
For more information, see [Embeddings](https://docs.coveo.com/en/n9de0370#embeddings).

Due to the amount of processing required for embeddings, the model is subject to the following embedding limits:

> **Note**
>
> For a given [model](https://docs.coveo.com/en/1012/), the same chunking strategy is used for all sources and item types.

* Up to 15 million items or 50 million chunks

> **Notes**
>
> * The maximum number of items depends on the [item allocation of your product plan](https://docs.coveo.com/en/l2590456#generative-ai-solutions).
> 
> * Coveo strongly recommends that you add a [Semantic Encoder (SE) model](https://docs.coveo.com/en/nb6a0483/) as part of your RGA implementation.
> If you have more than one RGA model in your Coveo organization, each RGA model must use only the items that are used by the SE model.

* 1000 chunks per item

> **Important**
>
> If an item is long with a lot of text, such as more than 200,000 words or 250 pages, the [model](https://docs.coveo.com/en/1012/) will embed the item's text until the 1000-chunk limit is reached.
> The remaining text won't be embedded and therefore won't be used by the [model](https://docs.coveo.com/en/1012/).
> 
> To make sure that each item's text is fully embedded, follow [best practices](https://docs.coveo.com/en/nb6a0008#best-practices) by keeping items concise and focused.

* 250 words per chunk

> **Note**
>
> There can be an overlap of up to 10% between chunks.
> In other words, the last 10% of the previous chunk can be the first 10% of the next chunk.

## "Status" column

On the [**Models**](https://platform.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) ([platform-ca](https://platform-ca.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) | [platform-eu](https://platform-eu.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) | [platform-au](https://platform-au.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/)) page of the [Administration Console](https://docs.coveo.com/en/183/), the **Status** column indicates the current state of your Coveo ML models.

The following table lists the possible model statuses and their definitions:

[%header,cols="1,6,^.^1"]
|===
.^|Status
.^|Definition
|Status icon

|Active
|The model is active and available.
a|![Active](leverage-machine-learning/model-active.png)

|Build in progress
|The model is currently building.
a|![Building](leverage-machine-learning/model-build.png)

|Inactive
|The model isn't ready to be queried, such as when a model was recently created or the organization is offline.  
Click **See more details** for additional information (see [Review model information](https://docs.coveo.com/en/1894/)).
a|![Inactive](leverage-machine-learning/model-limited.png)

|Limited
|Build issues exist that may affect model performance.  
Click **See more details** for additional information (see [Review model information](https://docs.coveo.com/en/1894/)).
a|![Limited](leverage-machine-learning/model-limited.png)

|Soon to be archived
|The model will soon be archived because it hasn't been queried for an extended period of time.  
Click **Delete** to remove the model.  
[Learn more about archived models](https://docs.coveo.com/en/mb3e0324/).
a|![Archive pending](leverage-machine-learning/model-limited.png)

|Error
|An error prevented the model from being built successfully.  
If it's a temporary system error, check back soon.
Otherwise, click **See more details** for additional information (see [Review model information](https://docs.coveo.com/en/1894/)).
a|![Error](leverage-machine-learning/model-error.png)

|Archived
|The model was archived because it hasn't been queried for an extended period of time.  
Click **Delete** to remove the model.  
[Learn more about archived models](https://docs.coveo.com/en/mb3e0324/).
a|![Archived](leverage-machine-learning/model-archived.png)
|===

## Required privileges

By default, members with the [required privileges](https://docs.coveo.com/en/1832#required-privileges) can view and edit elements of the [**Models**](https://platform.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) ([platform-ca](https://platform-ca.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) | [platform-eu](https://platform-eu.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/) | [platform-au](https://platform-au.cloud.coveo.com/admin/#/orgid/ai-and-ml/models/)) page.

The following table indicates the privileges required for members to manage Coveo Generic models (see [Manage privileges](https://docs.coveo.com/en/3151/) and [Privilege reference](https://docs.coveo.com/en/1707/)).

[cols="3",options="header"]
|===
|Action
|Service - Domain
|Required access level

|View models
|Machine Learning - Models  
Organization - Organization  
Search - Query pipelines
|View

.5+|Manage models
|Organization - Organization  
Search - Query pipelines
|View

|Machine Learning - Models
|Edit

|Machine Learning - Allow content preview
|Enable

|Content - Sources
|View All

|Content - Fields
|View
|===

## What's next?

[Associate the RGA model with a query pipeline](https://docs.coveo.com/en/nb6a0104/).