add link in intro

Author: sdgilley

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
 

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:
 

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:
 

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