Content filtering overview

Azure OpenAI includes a content filtering system that works alongside core models, including image generation models. This system works by running both the prompt and completion through a set of classification models designed to detect and prevent the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. Variations in API configurations and application design might affect completions and thus filtering behavior.

In addition to the content filtering system, Azure OpenAI performs monitoring to detect content and/or behaviors that suggest use of the service in a manner that might violate applicable product terms. For more information about understanding and mitigating risks associated with your application, see the Transparency Note for Azure OpenAI. For more information about how data is processed for content filtering and abuse monitoring, see Data, privacy, and security for Azure OpenAI.

The articles in this section provide information about the content filtering categories, the filtering severity levels and their configurability, and API scenarios to be considered in application design and implementation.

Content filter types

The content filtering system integrated in the Azure OpenAI contains:

• Neural multi-class classification models aimed at detecting and filtering harmful content; the models cover four categories (hate, sexual, violence, and self-harm) across four severity levels (safe, low, medium, and high). Content detected at the 'safe' severity level is labeled in annotations but isn't subject to filtering and isn't configurable.
• Other optional classification models aimed at detecting jailbreak risk and known content for text and code; these models are binary classifiers that flag whether user or model behavior qualifies as a jailbreak attack or match to known text or source code. The use of these models is optional, but use of protected material code model may be required for Customer Copyright Commitment coverage.

Filter categories

The following table summarizes the risk categories supported by Azure OpenAI's content filtering system.

¹ If you're an owner of text material and want to submit text content for protection, file a request.

² Not available in non-streaming scenarios; only available for streaming scenarios. The following regions support Groundedness Detection: Central US, East US, France Central, and Canada East

Scenario details

When the content filtering system detects harmful content, you receive either an error on the API call if the prompt was deemed inappropriate, or the finish_reason on the response will be content_filter to signify that some of the completion was filtered. When building your application or system, you'll want to account for these scenarios where the content returned by the Completions API is filtered, which might result in content that is incomplete. How you act on this information will be application specific. The behavior can be summarized in the following points:

• Prompts that are classified at a filtered category and severity level will return an HTTP 400 error.
• Non-streaming completions calls won't return any content when the content is filtered. The finish_reason value is set to content_filter. In rare cases with longer responses, a partial result can be returned. In these cases, the finish_reason is updated.
• For streaming completions calls, segments are returned back to the user as they're completed. The service continues streaming until either reaching a stop token, length, or when content that is classified at a filtered category and severity level is detected.

Scenario: You send a non-streaming completions call asking for multiple outputs; no content is classified at a filtered category and severity level

The table below outlines the various ways content filtering can appear:

Example request payload:

{
    "prompt":"Text example", 
    "n": 3,
    "stream": false
}

Example response JSON:

{
    "id": "example-id",
    "object": "text_completion",
    "created": 1653666286,
    "model": "davinci",
    "choices": [
        {
            "text": "Response generated text",
            "index": 0,
            "finish_reason": "stop",
            "logprobs": null
        }
    ]
}

Scenario: Your API call asks for multiple responses (N>1) and at least one of the responses is filtered

Example request payload:

{
    "prompt":"Text example",
    "n": 3,
    "stream": false
}

Example response JSON:

{
    "id": "example",
    "object": "text_completion",
    "created": 1653666831,
    "model": "ada",
    "choices": [
        {
            "text": "returned text 1",
            "index": 0,
            "finish_reason": "length",
            "logprobs": null
        },
        {
            "text": "returned text 2",
            "index": 1,
            "finish_reason": "content_filter",
            "logprobs": null
        }
    ]
}

Scenario: An inappropriate input prompt is sent to the completions API (either for streaming or non-streaming)

Example request payload:

{
    "prompt":"Content that triggered the filtering model"
}

Example response JSON:

"error": {
    "message": "The response was filtered",
    "type": null,
    "param": "prompt",
    "code": "content_filter",
    "status": 400
}

Scenario: You make a streaming completions call; no output content is classified at a filtered category and severity level

Example request payload:

{
    "prompt":"Text example",
    "n": 3,
    "stream": true
}

Example response JSON:

{
    "id": "cmpl-example",
    "object": "text_completion",
    "created": 1653670914,
    "model": "ada",
    "choices": [
        {
            "text": "last part of generation",
            "index": 2,
            "finish_reason": "stop",
            "logprobs": null
        }
    ]
}

Scenario: You make a streaming completions call asking for multiple completions and at least a portion of the output content is filtered

Example request payload:

{
    "prompt":"Text example",
    "n": 3,
    "stream": true
}

Example response JSON:

 {
    "id": "cmpl-example",
    "object": "text_completion",
    "created": 1653670515,
    "model": "ada",
    "choices": [
        {
            "text": "Last part of generated text streamed back",
            "index": 2,
            "finish_reason": "content_filter",
            "logprobs": null
        }
    ]
}

Scenario: Content filtering system doesn't run on the completion

Example request payload:

{
    "prompt":"Text example",
    "n": 1,
    "stream": false
}

Example response JSON:

{
    "id": "cmpl-example",
    "object": "text_completion",
    "created": 1652294703,
    "model": "ada",
    "choices": [
        {
            "text": "generated text",
            "index": 0,
            "finish_reason": "length",
            "logprobs": null,
            "content_filter_results": {
                "error": {
                    "code": "content_filter_error",
                    "message": "The contents are not filtered"
                }
            }
        }
    ]
}

Best practices

As part of your application design, consider the following best practices to deliver a positive experience with your application while minimizing potential harms:

• Decide how you want to handle scenarios where your users send prompts containing content that is classified at a filtered category and severity level or otherwise misuse your application.
• Check the finish_reason to see if a completion is filtered.
• Check that there's no error object in the content_filter_results (indicating that content filters didn't run).
• If you're using the protected material code model in annotate mode, display the citation URL when you're displaying the code in your application.

Related content

• Learn about the content filtering categories and severity levels.
• Learn more about the underlying models that power Azure OpenAI.
• Apply for modified content filters via this form.
• Azure OpenAI content filtering is powered by Azure AI Content Safety.
• Learn more about understanding and mitigating risks associated with your application: Overview of Responsible AI practices for Azure OpenAI models.
• Learn more about how data is processed in connection with content filtering and abuse monitoring: Data, privacy, and security for Azure OpenAI.