Merge pull request #3226 from sdgilley/sdg-update-links

prmerger-automator[bot] · web-flow · commit da2f856c6da3 · 2025-02-27T22:25:47.000Z
add link to intros
diff --git a/articles/ai-studio/faq.yml b/articles/ai-studio/faq.yml
@@ -14,7 +14,7 @@ metadata:
   author: sdgilley
 title: Azure AI Foundry frequently asked questions
 summary: |
-  If you can't find answers to your questions in this document, and still need help check the [Azure AI services support options guide](../ai-services/cognitive-services-support-options.md?context=/azure/ai-services/openai/context/context). Azure OpenAI is part of Azure AI services.
+  FAQ for [Azure AI Foundry](https://ai.azure.com). If you can't find answers to your questions in this document, and still need help check the [Azure AI services support options guide](../ai-services/cognitive-services-support-options.md?context=/azure/ai-services/openai/context/context). Azure OpenAI is part of Azure AI services.
 sections:
   - name: General questions
     questions:
diff --git a/articles/ai-studio/how-to/create-manage-compute-session.md b/articles/ai-studio/how-to/create-manage-compute-session.md
@@ -20,7 +20,7 @@ author: sdgilley
 
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
-You need a compute session to run [prompt flows](prompt-flow.md). Use Azure AI Foundry to create and manage prompt flow compute sessions.
+You need a compute session to run [prompt flows](prompt-flow.md). Use [Azure AI Foundry](https://ai.azure.com) to create and manage prompt flow compute sessions.
 
 A prompt flow compute session has computing resources that are required for the application to run, including a Docker image that contains all necessary dependency packages. In addition to flow execution, Azure AI Foundry uses the compute session to ensure the accuracy and functionality of the tools incorporated within the flow when you make updates to the prompt or code content.
 
diff --git a/articles/ai-studio/how-to/develop/create-hub-project-sdk.md b/articles/ai-studio/how-to/develop/create-hub-project-sdk.md
@@ -16,7 +16,7 @@ author: sdgilley
 
 [!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
-In this article, you learn how to create the following Azure AI Foundry resources using the Azure Machine Learning SDK and Azure CLI (with machine learning extension):
+In this article, you learn how to create the following [Azure AI Foundry](https://ai.azure.com) resources using the Azure Machine Learning SDK and Azure CLI (with machine learning extension):
 - An Azure AI Foundry hub
 - An Azure AI Services connection
 
diff --git a/articles/ai-studio/how-to/develop/vscode.md b/articles/ai-studio/how-to/develop/vscode.md
@@ -20,7 +20,7 @@ author: sdgilley
 
 [!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
-Azure AI Foundry supports developing in VS Code - Desktop and Web. In each scenario, your VS Code instance is remotely connected to a prebuilt custom container running on a virtual machine, also known as a compute instance.
+[Azure AI Foundry](https://ai.azure.com) supports developing in VS Code - Desktop and Web. In each scenario, your VS Code instance is remotely connected to a prebuilt custom container running on a virtual machine, also known as a compute instance.
 
 ## Launch VS Code from Azure AI Foundry
 
diff --git a/articles/ai-studio/quickstarts/get-started-code.md b/articles/ai-studio/quickstarts/get-started-code.md
@@ -16,7 +16,7 @@ author: sdgilley
 
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
-In this quickstart, we walk you through setting up your local development environment with the Azure AI Foundry SDK. We write a prompt, run it as part of your app code, trace the LLM calls being made, and run a basic evaluation on the outputs of the LLM.
+In this quickstart, we walk you through setting up your local development environment with the [Azure AI Foundry](https://ai.azure.com) SDK. We write a prompt, run it as part of your app code, trace the LLM calls being made, and run a basic evaluation on the outputs of the LLM.
 
 ## Prerequisites
 
diff --git a/articles/ai-studio/tutorials/copilot-sdk-build-rag.md b/articles/ai-studio/tutorials/copilot-sdk-build-rag.md
@@ -15,7 +15,7 @@ ms.custom: copilot-learning-hub, ignite-2024
 
 # Tutorial:  Part 2 - Build a custom knowledge retrieval (RAG) app with the Azure AI Foundry SDK
 
-In this tutorial, you use the Azure AI Foundry SDK (and other libraries) to build, configure, and evaluate a chat app for your retail company called Contoso Trek. Your retail company specializes in outdoor camping gear and clothing. The chat app should answer questions about your products and services. For example, the chat app can answer questions such as "which tent is the most waterproof?" or "what is the best sleeping bag for cold weather?".
+In this tutorial, you use the [Azure AI Foundry](https://ai.azure.com) SDK (and other libraries) to build, configure, and evaluate a chat app for your retail company called Contoso Trek. Your retail company specializes in outdoor camping gear and clothing. The chat app should answer questions about your products and services. For example, the chat app can answer questions such as "which tent is the most waterproof?" or "what is the best sleeping bag for cold weather?".
 
 This part two shows you how to enhance a basic chat application by adding [retrieval augmented generation (RAG)](../concepts/retrieval-augmented-generation.md) to ground the responses in your custom data. Retrieval Augmented Generation (RAG) is a pattern that uses your data with a large language model (LLM) to generate answers specific to your data. In this part two, you learn how to:
 
diff --git a/articles/ai-studio/tutorials/copilot-sdk-create-resources.md b/articles/ai-studio/tutorials/copilot-sdk-create-resources.md
@@ -16,7 +16,7 @@ author: sdgilley
 
 # Tutorial:  Part 1 - Set up project and development environment to build a custom knowledge retrieval (RAG) app with the Azure AI Foundry SDK
 
-In this tutorial, you use the Azure AI Foundry SDK (and other libraries) to build, configure, and evaluate a chat app for your retail company called Contoso Trek. Your retail company specializes in outdoor camping gear and clothing. The chat app should answer questions about your products and services. For example, the chat app can answer questions such as "which tent is the most waterproof?" or "what is the best sleeping bag for cold weather?".
+In this tutorial, you use the [Azure AI Foundry](https://ai.azure.com) SDK (and other libraries) to build, configure, and evaluate a chat app for your retail company called Contoso Trek. Your retail company specializes in outdoor camping gear and clothing. The chat app should answer questions about your products and services. For example, the chat app can answer questions such as "which tent is the most waterproof?" or "what is the best sleeping bag for cold weather?".
 
 This tutorial is part one of a three-part tutorial.  This part one gets you ready to write code in part two and evaluate your chat app in part three. In this part, you:
 
diff --git a/articles/ai-studio/tutorials/copilot-sdk-evaluate.md b/articles/ai-studio/tutorials/copilot-sdk-evaluate.md
@@ -16,7 +16,7 @@ author: sdgilley
 
 # Tutorial: Part 3 - Evaluate a custom chat application with the Azure AI Foundry SDK
 
-In this tutorial, you use the Azure AI SDK (and other libraries) to  evaluate the chat app you built in [Part 2 of the tutorial series](copilot-sdk-build-rag.md). In this part three, you learn how to:
+In this tutorial, you use the [Azure AI Foundry](https://ai.azure.com) SDK (and other libraries) to  evaluate the chat app you built in [Part 2 of the tutorial series](copilot-sdk-build-rag.md). In this part three, you learn how to:
 
 > [!div class="checklist"]
 > - Create an evaluation dataset
