fixing links

Author: Blackmist

articles/ai-foundry/model-inference/how-to/use-blocklists.md

@@ -28,7 +28,7 @@ The configurable content filters are sufficient for most content moderation need
     > [!NOTE]
     > Blocklist (preview) is only supported for Azure OpenAI models.
 
-[!INCLUDE [use-blocklists](../../../ai-studio/includes/use-blocklists.md)]
+[!INCLUDE [use-blocklists](../../includes/use-blocklists.md)]
 
 ## Next steps
 

articles/ai-foundry/model-inference/includes/configure-content-filters/intro.md

@@ -7,7 +7,7 @@ ms.date: 1/21/2025
 ms.topic: include
 ---
 
-[!INCLUDE [Feature preview](../../../../ai-studio/includes/feature-preview.md)]
+[!INCLUDE [Feature preview](../../../includes/feature-preview.md)]
 
 The content filtering system integrated into Azure AI Services runs alongside the core models. It uses an ensemble of multi-class classification models to detect four categories of harmful content (violence, hate, sexual, and self-harm) at four severity levels respectively (safe, low, medium, and high). It offers optional binary classifiers for detecting jailbreak risk, existing text, and code in public repositories. Learn more about content categories, severity levels, and the behavior of the content filtering system [in the following article](../../concepts/content-filter.md)
 

articles/ai-foundry/model-inference/includes/configure-entra-id/intro.md

@@ -7,7 +7,7 @@ ms.date: 01/23/2025
 ms.topic: include
 ---
 
-[!INCLUDE [Feature preview](../../../../ai-studio/includes/feature-preview.md)]
+[!INCLUDE [Feature preview](../../../includes/feature-preview.md)]
 
 Models deployed to Azure AI model inference in Azure AI Services support key-less authorization using Microsoft Entra ID. Key-less authorization enhances security, simplifies the user experience, reduces operational complexity, and provides robust compliance support for modern development. It makes it a strong choice for organizations adopting secure and scalable identity management solutions.
 

articles/ai-foundry/model-inference/includes/configure-project-connection/intro.md

@@ -7,7 +7,7 @@ ms.date: 1/21/2025
 ms.topic: include
 ---
 
-[!INCLUDE [Feature preview](../../../../ai-studio/includes/feature-preview.md)]
+[!INCLUDE [Feature preview](../../../includes/feature-preview.md)]
 
 You can use Azure AI model inference in your projects in Azure AI Foundry to create reach applications and interact/manage the models available. To use the Azure AI model inference service in your project, you need to create a connection to the Azure AI Services resource.
 

articles/ai-foundry/model-inference/includes/create-model-deployments/intro.md

@@ -7,7 +7,7 @@ ms.date: 1/21/2025
 ms.topic: include
 ---
 
-[!INCLUDE [Feature preview](../../../../ai-studio/includes/feature-preview.md)]
+[!INCLUDE [Feature preview](../../../includes/feature-preview.md)]
 
 You can decide and configure which models are available for inference in the inference endpoint. When a given model is configured, you can then generate predictions from it by indicating its model name or deployment name on your requests. No further changes are required in your code to use it.
 

articles/ai-foundry/model-inference/includes/create-resources/intro.md

@@ -7,7 +7,7 @@ ms.date: 1/21/2025
 ms.topic: include
 ---
 
-[!INCLUDE [Feature preview](../../../../ai-studio/includes/feature-preview.md)]
+[!INCLUDE [Feature preview](../../../includes/feature-preview.md)]
 
 In this article, you learn how to create the resources required to use Azure AI model inference and consume flagship models from Azure AI model catalog.