fix term

Author: PatrickFarley

articles/ai-foundry/concepts/content-filtering.md

@@ -26,7 +26,7 @@ author: PatrickFarley
 
 The content filtering system is powered by [Azure AI Content Safety](../../ai-services/content-safety/overview.md), and it works by running both the prompt input and completion output through a set of classification models designed to detect and prevent the output of harmful content. Variations in API configurations and application design might affect completions and thus filtering behavior.
 
-With Azure OpenAI model deployments, you can use the default content filter or create your own content filter (described later on). Models available through **serverless APIs** have content filtering enabled by default. To learn more about the default content filter enabled for serverless APIs, see [Guidelines & controls for models curated by Azure AI in the model catalog](model-catalog-content-safety.md).
+With Azure OpenAI model deployments, you can use the default content filter or create your own content filter (described later on). Models available through **serverless APIs** have content filtering enabled by default. To learn more about the default content filter enabled for serverless APIs, see [Guardrails & controls for models curated by Azure AI in the model catalog](model-catalog-content-safety.md).
 
 ## Language support
 

articles/ai-foundry/concepts/model-catalog-content-safety.md

@@ -1,7 +1,7 @@
 ---
-title: Guidelines & controls for models curated by Azure AI in the model catalog
+title: Guardrails & controls for models curated by Azure AI in the model catalog
 titleSuffix: Azure AI Foundry
-description: Learn about Guidelines & controls for models deployed using serverless APIs, using Azure AI Foundry.
+description: Learn about Guardrails & controls for models deployed using serverless APIs, using Azure AI Foundry.
 manager: scottpolly
 ms.service: azure-ai-foundry
 ms.topic: conceptual
@@ -13,18 +13,18 @@ reviewer: ositanachi
 ms.custom: 
 ---
 
-# Guidelines & controls for models curated by Azure AI in the model catalog
+# Guardrails & controls for models curated by Azure AI in the model catalog
 
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
-In this article, learn about Guidelines & controls capabilities for models from the model catalog deployed using serverless APIs.
+In this article, learn about Guardrails & controls capabilities for models from the model catalog deployed using serverless APIs.
 
 
 ## Content filter defaults
 
 Azure AI uses a default configuration of [Azure AI Content Safety](/azure/ai-services/content-safety/overview) content filters to detect harmful content across four categories including hate and fairness, self-harm, sexual, and violence for models deployed via serverless APIs. To learn more about content filtering (preview), see [Understand harm categories](#understand-harm-categories).
 
-The default content filtering configuration for text models is set to filter at the medium severity threshold, filtering any detected content at this level or higher. For image models, the default content filtering configuration is set at the low configuration threshold, filtering at this level or higher. For models deployed using the [Azure AI model inference service](../../ai-foundry/model-inference/how-to/configure-content-filters.md), you can create configurable filters by selecting the **Content filters** tab within the **Safety + security** page of the Azure AI Foundry portal.
+The default content filtering configuration for text models is set to filter at the medium severity threshold, filtering any detected content at this level or higher. For image models, the default content filtering configuration is set at the low configuration threshold, filtering at this level or higher. For models deployed using the [Azure AI model inference service](../../ai-foundry/model-inference/how-to/configure-content-filters.md), you can create configurable filters by selecting the **Content filters** tab within the **Guardrails & controls** page of the Azure AI Foundry portal.
 
 > [!TIP]
 > Content filtering (preview) isn't available for certain model types that are deployed via serverless APIs. These model types include embedding models and time series models.

articles/ai-foundry/concepts/models-featured.md

@@ -364,5 +364,5 @@ For examples of how to use Stability AI models, see the following examples:
 - [Deploy models as serverless APIs](../how-to/deploy-models-serverless.md)
 - [Model catalog and collections in Azure AI Foundry portal](../how-to/model-catalog-overview.md)
 - [Region availability for models in serverless API endpoints](../how-to/deploy-models-serverless-availability.md)
-- [Guidelines & controls for models curated by Azure AI in the model catalog](model-catalog-content-safety.md)
+- [Guardrails & controls for models curated by Azure AI in the model catalog](model-catalog-content-safety.md)
 

articles/ai-foundry/how-to/deploy-models-gretel-navigator.md

@@ -233,7 +233,7 @@ result = client.complete(
 ```
 
 
-### Apply Guidelines and controls
+### Apply Guardrails and controls
 
 The Azure AI model inference API supports [Azure AI Content Safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI Content Safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering (preview) system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions.
 
@@ -477,7 +477,7 @@ The following example request shows other parameters that you can specify in the
 }
 ```
 
-### Apply Guidelines & controls
+### Apply Guardrails & controls
 
 The Azure AI model inference API supports [Azure AI Content Safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI Content Safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering (preview) system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions.
 

articles/ai-foundry/how-to/develop/evaluate-sdk.md

@@ -508,7 +508,7 @@ Output:
 
 ```
 
-The result of the Guidelines & controls evaluators for a query and response pair is a dictionary containing:
+The result of the Guardrails & controls evaluators for a query and response pair is a dictionary containing:
 
 - `{metric_name}` provides a severity label for that content risk ranging from Very low, Low, Medium, and High. To learn more about the descriptions of each content risk and severity scale, see [Evaluation and monitoring metrics for generative AI](../../concepts/evaluation-metrics-built-in.md).
 - `{metric_name}_score` has a range between 0 and 7 severity level that maps to a severity label given in `{metric_name}`.

articles/ai-foundry/how-to/flow-process-image.md

@@ -113,8 +113,8 @@ Assume you want to build a chatbot that can answer any questions about the image
     In this example, `{{question}}` refers to the chat input, which is a list of texts and images.
 1. In *Outputs*, change the value of "answer" to the name of your vision tool's output, for example, `${gpt_vision.output}`.
     :::image type="content" source="../media/prompt-flow/how-to-process-image/chat-output-definition.png" alt-text="Screenshot of chat output type configuration." lightbox = "../media/prompt-flow/how-to-process-image/chat-output-definition.png":::  
-1. (Optional) You can add any custom logic to the flow to process the GPT-4V output. For example, you can add Guidelines & controls tool to detect if the answer contains any inappropriate content, and return a final answer to the user.
-    :::image type="content" source="../media/prompt-flow/how-to-process-image/chat-flow-postprocess.png" alt-text="Screenshot of processing gpt-4v output with Guidelines & controls tool." lightbox = "../media/prompt-flow/how-to-process-image/chat-flow-postprocess.png":::
+1. (Optional) You can add any custom logic to the flow to process the GPT-4V output. For example, you can add Guardrails & controls tool to detect if the answer contains any inappropriate content, and return a final answer to the user.
+    :::image type="content" source="../media/prompt-flow/how-to-process-image/chat-flow-postprocess.png" alt-text="Screenshot of processing gpt-4v output with Guardrails & controls tool." lightbox = "../media/prompt-flow/how-to-process-image/chat-flow-postprocess.png":::
 1. Now you can **test the chatbot**. Open the chat window, and input any questions with images. The chatbot will answer the questions based on the image and text inputs. The chat input value is automatically backfilled from the input in the chat window. You can find the texts with images in the chat box which is translated into a list of texts and images.
     :::image type="content" source="../media/prompt-flow/how-to-process-image/chatbot-test.png" alt-text="Screenshot of chatbot interaction with images." lightbox = "../media/prompt-flow/how-to-process-image/chatbot-test.png":::
 

articles/ai-foundry/how-to/model-catalog-overview.md

@@ -68,7 +68,7 @@ Features | Managed compute | Serverless API (pay-per-token)
 --|--|--
 Deployment experience and billing | Model weights are deployed to dedicated virtual machines with managed compute. A managed compute, which can have one or more deployments, makes available a REST API for inference. You're billed for the virtual machine core hours that the deployments use. | Access to models is through a deployment that provisions an API to access the model. The API provides access to the model that Microsoft hosts and manages, for inference. You're billed for inputs and outputs to the APIs, typically in tokens. Pricing information is provided before you deploy.
 API authentication | Keys and Microsoft Entra authentication. | Keys only.
-Guidelines & controls | Use Azure AI Content Safety service APIs. | Azure AI Content Safety filters are available integrated with inference APIs. Azure AI Content Safety filters are billed separately.
+Guardrails & controls | Use Azure AI Content Safety service APIs. | Azure AI Content Safety filters are available integrated with inference APIs. Azure AI Content Safety filters are billed separately.
 Network isolation | [Configure managed networks for Azure AI Foundry hubs](configure-managed-network.md).  | Managed compute follow your hub's public network access (PNA) flag setting. For more information, see the [Network isolation for models deployed via Serverless APIs](#network-isolation-for-models-deployed-via-serverless-apis) section later in this article.
 
 ### Available models for supported deployment options
@@ -119,7 +119,7 @@ Learn more about deploying models:
 
 The *prompt flow* feature in Azure Machine Learning offers a great experience for prototyping. You can use models deployed with managed compute in prompt flow with the [Open Model LLM tool](/azure/machine-learning/prompt-flow/tools-reference/open-model-llm-tool). You can also use the REST API exposed by managed compute in popular LLM tools like LangChain with the [Azure Machine Learning extension](https://python.langchain.com/docs/integrations/chat/azureml_chat_endpoint/).  
 
-### Guidelines & controls for models deployed as managed compute
+### Guardrails & controls for models deployed as managed compute
 
 The [Azure AI Content Safety](../../ai-services/content-safety/overview.md) service is available for use with managed compute to screen for various categories of harmful content, such as sexual content, violence, hate, and self-harm. You can also use the service to screen for advanced threats such as jailbreak risk detection and protected material text detection.
 
@@ -162,7 +162,7 @@ In Azure AI Foundry portal, you can use vector indexes and retrieval-augmented g
 
 Pay-per-token billing is available only to users whose Azure subscription belongs to a billing account in a country/region where the model provider has made the offer available. If the offer is available in the relevant region, the user then must have a project resource in the Azure region where the model is available for deployment or fine-tuning, as applicable. See [Region availability for models in serverless API endpoints | Azure AI Foundry](deploy-models-serverless-availability.md) for detailed information.
 
-### Guidelines & controls for models deployed via serverless APIs
+### Guardrails & controls for models deployed via serverless APIs
 
 [!INCLUDE [content-safety-serverless-models](../includes/content-safety-serverless-models.md)]
 

articles/ai-foundry/how-to/prompt-flow-tools/content-safety-tool.md

@@ -16,7 +16,7 @@ ms.collection: ce-skilling-ai-copilot, ce-skilling-fresh-tier1
 ms.update-cycle: 180-days
 ---
 
-# Guidelines & controls tool for flows in Azure AI Foundry portal
+# Guardrails & controls tool for flows in Azure AI Foundry portal
 
 [!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 

articles/ai-foundry/includes/content-safety-serverless-apis-note.md

@@ -13,4 +13,4 @@ ms.custom: include file
 ---
 
 > [!NOTE]
-> Azure AI Content Safety is currently available for models deployed as standard deployment, but not to models deployed via managed compute. To learn more about Azure AI Content Safety for models deployed as standard deployment, see [Guidelines & controls for models curated by Azure AI in the model catalog](../concepts/model-catalog-content-safety.md).
+> Azure AI Content Safety is currently available for models deployed as standard deployment, but not to models deployed via managed compute. To learn more about Azure AI Content Safety for models deployed as standard deployment, see [Guardrails & controls for models curated by Azure AI in the model catalog](../concepts/model-catalog-content-safety.md).

articles/ai-foundry/includes/content-safety-serverless-models.md

@@ -13,7 +13,7 @@ ms.custom: include file
 # Also used in Azure Machine Learning documentation
 ---
 
-For language models deployed via standard deployment, Azure AI implements a default configuration of [Azure AI Content Safety](../../ai-services/content-safety/overview.md) text moderation filters that detect harmful content such as hate, self-harm, sexual, and violent content. To learn more about content filtering (preview), see [Guidelines & controls for models curated by Azure AI in the model catalog](../concepts/model-catalog-content-safety.md). 
+For language models deployed via standard deployment, Azure AI implements a default configuration of [Azure AI Content Safety](../../ai-services/content-safety/overview.md) text moderation filters that detect harmful content such as hate, self-harm, sexual, and violent content. To learn more about content filtering (preview), see [Guardrails & controls for models curated by Azure AI in the model catalog](../concepts/model-catalog-content-safety.md). 
 
 > [!TIP]
 > Content filtering (preview) is not available for certain model types that are deployed via serverless APIs. These model types include embedding models and time series models.