---
title: Relevance Generative Answering (RGA) content requirements and best practices
slug: nb6a0008
canonical_url: https://docs.coveo.com/en/nb6a0008/
collection: leverage-machine-learning
source_format: adoc
---
# Relevance Generative Answering (RGA) content requirements and best practices
[Relevance Generative Answering (RGA)](https://docs.coveo.com/en/n9de0370/) uses your enterprise content as the data from which to generate answers.
The content that you choose to use for RGA, and the quality of that content, has a direct impact on the quality of the answers that are generated by RGA.

An RGA implementation requires you to [create an RGA model](https://docs.coveo.com/en/nb6a0085/).
When creating the [model](https://docs.coveo.com/en/1012/), you must specify the [indexed](https://docs.coveo.com/en/204/) content that the model will use to generate answers.

This article describes the requirements and best practices with regards to the content that you choose to use 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.

## How RGA uses your content

Before deciding on the content to use for RGA, it's important to have a basic understanding of how your content is used to generate answers.

When an [item](https://docs.coveo.com/en/210/) is [indexed](https://docs.coveo.com/en/204/), the item's content is mapped to the [`body`](https://docs.coveo.com/en/1847#item-body) [field](https://docs.coveo.com/en/200/) in the Coveo [index](https://docs.coveo.com/en/204/).
The [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/) uses a pre-trained sentence transformer language model to convert your indexed content's [body](https://docs.coveo.com/en/3313/) text into mathematical representations ([vectors](https://docs.coveo.com/en/nccf9008/)) in a process called [embedding](https://docs.coveo.com/en/n9de0370#embeddings).
When a user enters a [query](https://docs.coveo.com/en/231/), the model uses the vector space to retrieve the most relevant content.
This retrieval is based on semantic similarity using [embeddings](https://docs.coveo.com/en/ncc87383/), which are created using the text in your content's [body](https://docs.coveo.com/en/3313/) text.

In summary, the [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/) parses your content's [body](https://docs.coveo.com/en/3313/) text when creating the embeddings, and uses only that content to generate answers.
Therefore, an item's [body](https://docs.coveo.com/en/3313/) data should be as clean, focused, and relevant as possible.
The better the data, the better the embeddings, and the better the answers.
For best results, you should adhere to the requirements and best practices detailed in this article when choosing the content to use for RGA.

For more information on how your content is used to generate answers, see [RGA processes](https://docs.coveo.com/en/n9de0370#rga-processes).

> **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.

## Requirements

* The content you want to use must be [indexed](https://docs.coveo.com/en/204/) in your Coveo [organization](https://docs.coveo.com/en/185/) before creating the [model](https://docs.coveo.com/en/1012/).

You don't have to use all the content in your index.
In fact, best practices dictate that you should choose a reasonably sized dataset to keep the content focused and relevant.
When creating the model, you can choose to use a subset of your indexed content by selecting the [sources](https://docs.coveo.com/en/246/) that contain the [items](https://docs.coveo.com/en/210/), and then further filtering the source dataset.
For more information, see [Choose your content](#choose-your-content).

> **Note**
>
> If the indexed items you want to use aren't [optimized for use](#optimize-your-content) with the model, re-index the items with the proper configuration.

* An indexed item must contain a unique value in the [`permanentid`](https://docs.coveo.com/en/1913/) field in order for the item's content to be embedded and used by the model.

> **Note**
>
> By default, an item indexed using a standard Coveo source automatically contains a value in its `permanentid` field that Coveo uses as the item's unique identifier.
> However, if you're using a custom source, such as Push API, you must make sure that the items that you want to use contain a unique value in the `permanentid` field.
> If not, you must [map unique metadata to the item's `permanentid` field](https://docs.coveo.com/en/1913#taking-advantage-of-the-permanentid-field).
> 
> To verify if an item contains a unique value in the `permanentid` field, you can use the [**Content Browser**](https://platform.cloud.coveo.com/admin/#/orgid/content/browser/) ([platform-ca](https://platform-ca.cloud.coveo.com/admin/#/orgid/content/browser/) | [platform-eu](https://platform-eu.cloud.coveo.com/admin/#/orgid/content/browser/) | [platform-au](https://platform-au.cloud.coveo.com/admin/#/orgid/content/browser/)) page of the [Coveo Administration Console](https://docs.coveo.com/en/183/) to [check the item's properties](https://docs.coveo.com/en/1712/).

* The indexed item's `language` field is English.

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

> **Note**
>
> To verify an item's `language` field, you can use the [**Content Browser**](https://platform.cloud.coveo.com/admin/#/orgid/content/browser/) ([platform-ca](https://platform-ca.cloud.coveo.com/admin/#/orgid/content/browser/) | [platform-eu](https://platform-eu.cloud.coveo.com/admin/#/orgid/content/browser/) | [platform-au](https://platform-au.cloud.coveo.com/admin/#/orgid/content/browser/)) page of the [Coveo Administration Console](https://docs.coveo.com/en/183/) to [check the item's properties](https://docs.coveo.com/en/1712/).

### Supported file types

Coveo has tested and supports the following file types for use with the [model](https://docs.coveo.com/en/1012/):

* HTML

* PDF

> **Notes**
>
> * Other text-based file types that are [supported at ingestion](https://docs.coveo.com/en/1689/) that aren't listed above may also provide good results, however, they're not officially supported by Coveo for use with the model.
> 
> * PDFs with single-column uninterrupted paragraph-based text sections provide best results.
> Text in tables and multi-columned text are embedded but parsing the text is more unpredictable.
> 
> * You can use the [optical character recognition (OCR) source feature](https://docs.coveo.com/en/2937/) to extract text from images in PDFs and image files.
> Otherwise, text from images won't be embedded or used by the model.
> 
> * Video files aren't supported.

## Best practices

This section describes best practices when it comes to choosing the content to use for the [model](https://docs.coveo.com/en/1012/) and how to optimize the content for best results.

It also covers strategies for presenting the generated answer to enhance the user experience and avoid potential liabilities.

### Choose your content

When deciding on the content to use, consider the following:

* Prioritize content that's designed to answer questions such as knowledge base articles, support documents, FAQs, community answers, and product documentation.

* Prioritize content that's written in a conversational tone.

* Prioritize shorter documents that are focused on a single topic.

> **Note**
>
> Avoid using very long documents that cover multiple topics.
> This may result in text being embedded as semantically similar, even though the context or topic is different.

* Content should be written using a single language.

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

> **Note**
>
> Other languages are supported as a beta.
> For a list of beta-supported languages, see [Supported languages for machine learning models](https://docs.coveo.com/en/1956#supported-languages-for-machine-learning-models).

* Avoid multiple documents with similar content.

* Choose a reasonably sized dataset to keep the content focused and current.

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

### Optimize your content

To optimize your content for the [model](https://docs.coveo.com/en/1012/), follow these best practices:

* Ensure that boilerplate content, such as headers, footers, and extra navigation elements, is removed from the `[body](https://docs.coveo.com/en/3313/)` data when the [items](https://docs.coveo.com/en/210/) are [indexed](https://docs.coveo.com/en/204/).
* Review the `body` data and source mappings to make sure that the body contains the desired content.

> **Notes**
>
> * For Web and Sitemap sources, you can use a [web scraping configuration](https://docs.coveo.com/en/mc1f3573/) to remove boilerplate content and select the web content to index.
> 
> * For all other types of sources, you can [edit an item's body mapping](https://docs.coveo.com/en/1847#add-or-edit-an-item-body-mapping) to make sure that the body contains the desired content.
### Add a disclaimer

Coveo strongly recommends the inclusion of a disclaimer for the answer generated by RGA, particularly in public-facing use cases.

The purpose of this disclaimer is to prompt users to use the citation links to refer to the original sources of information in your [indexed](https://docs.coveo.com/en/204/) content.
This precaution helps mitigate the risk of misinformation resulting from potential inaccuracies in the generated answer.

A disclaimer is included by default in the [RGA search interface component](https://docs.coveo.com/en/nb6a0037#rga-component-features) when using the hosted search page builder, hosted Insight Panel builder, In-Product Experience (IPX) builder, or the latest versions of the Coveo [Atomic](https://docs.coveo.com/en/lcdf0264/) library or Coveo [Quantic](https://docs.coveo.com/en/n9790069/) libraries.

![Relevance Generative Answering disclaimer](https://docs.coveo.com/en/assets/images/leverage-machine-learning/rga-legal-disclaimer-without-rephrase.png)

Coveo advises consulting with your company's legal counsel to obtain approval of the final wording.

## When is an answer not generated?

Adhering to the requirements and best practices outlined in this article greatly improves the relevancy of the answers that are generated by [RGA](https://docs.coveo.com/en/nbtb6010/).
In certain cases, however, an answer can't be generated for a given user query.
This can be caused either by [insufficient relevant content](#insufficient-relevant-content) or [not sorting by relevance](#not-sorting-by-relevance).

When an answer isn't generated, the [RGA component](https://docs.coveo.com/en/nb6a0037#rga-component-features) doesn't appear at all in the search results.

> **Note**
>
> If you're using a search interface that was created using the [Atomic library](https://docs.coveo.com/en/lcdf0264/) or [Quantic library](https://docs.coveo.com/en/n9790069/), you can [show a custom message](https://docs.coveo.com/en/nb6a0037#show-a-message-when-an-answer-is-not-generated) when an answer isn't generated for a user query.

### Insufficient relevant content

The segments of text ([chunks](https://docs.coveo.com/en/n9de0370#chunking)) that are used to generate an answer must meet a minimum similarity threshold with the user query.
An initial verification is made by the [RGA](https://docs.coveo.com/en/nbtb6010/) [model](https://docs.coveo.com/en/1012/) when retrieving the most relevant chunks during [second-stage content retrieval](https://docs.coveo.com/en/n9de0370#second-stage-content-retrieval), and a second verification is made by the generative large language model (LLM) when [generating the answer](https://docs.coveo.com/en/n9de0370#answer-generation).

If all of the chunks identified during second-stage content retrieval don't meet [RGA](https://docs.coveo.com/en/nbtb6010/)’s minimum similarity threshold with the user query, an answer isn't generated because the prompt isn't created or sent to the generative LLM (see [Answer generation](https://docs.coveo.com/en/n9de0370#answer-generation) for details on this process).

![Relevance Generative Answering no result | Coveo](https://docs.coveo.com/en/assets/images/leverage-machine-learning/rga-no-component.png)

> **Tip**
>
> You can use the [**Chunk relevancy threshold**](https://docs.coveo.com/en/nb6a0104#chunk-relevancy-threshold) model association option to modify the relevancy threshold that's used to determine whether a chunk is relevant enough to be considered by the RGA model for answer generation.

### Not sorting by relevance

If your search interface includes a sorting option, [Relevance Generative Answering (RGA)](https://docs.coveo.com/en/nbtb6010/) works best when results are sorted by relevance, which is the default sorting option.
Otherwise, an answer may not be generated.

![Search page sorting option | Coveo](https://docs.coveo.com/en/assets/images/leverage-machine-learning/rga-relevance-sorting-option.png)
For more information on the reasons why an answer wouldn't be generated, see [When is an answer not generated?](https://docs.coveo.com/en/nb6a0008#when-is-an-answer-not-generated).

## 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.

## What's next?

[Create an RGA model](https://docs.coveo.com/en/nb6a0085/).