Merge pull request #26 from Azure/miyagi-v2

Azure AI Foundry renaming and formatting fix

docs/wksp/05-semantic-kernel-workshop/simple-ai-orchestration/index.md

@@ -3,6 +3,7 @@
 This hands on workshop goes through the following lessons for creating a simple Semantic Kernel
 chatbot as a console application:
 
+1. [Prerequisites](pre-reqs.md)
 1. [Lesson 1: Create Simple Semantic Kernel chatbot](lesson1.md)
 1. [Lesson 2: Create Simple Semantic Kernel chatbot with history](lesson2.md)
 1. [Lesson 3: Create Simple Semantic Kernel chatbot with plugins](lesson3.md)

docs/wksp/05-semantic-kernel-workshop/simple-ai-orchestration/pre-reqs.md

@@ -53,9 +53,7 @@ cd intelligent-app-workshop
 1. Create Azure OpenAI Service and retrieve the Endpoint URL, API Key and deployed model name then update newly created `appsettings.json`
 
     1. Get Azure OpenAI access values (from Azure Portal):
-
-        First we need to create a new Azure OpenAI Service, so let's start there.
-        1. Go to the [Azure Portal](https://portal.azure.com).
+        1. First we need to create a new Azure OpenAI Service, so let's start there. Go to the [Azure Portal](https://portal.azure.com).
         1. Click on [Create A Resource](https://ms.portal.azure.com/#create/hub)
         1. On the search bar type **Azure OpenAI** and hit enter
         1. Locate **Azure OpenAI** and click **Create**
@@ -82,13 +80,13 @@ cd intelligent-app-workshop
 
         1. Click the **Model deployments** option in the sidebar (left menu) for Azure OpenAI resource.
         1. In the destination page, click **Manage Deployments**
-        1. (Optional) You can directly navigate to the [Azure OpenAI Studio website](https://oai.azure.com).
+        1. (Optional) You can directly navigate to the [Azure AI Foundry portal](https://oai.azure.com).
 
-        This will take you to the Azure OpenAI Studio website, where we'll find the other values as described below.
+        This will take you to the Azure AI Foundry website, where we'll find the other values as described below.
 
-    1. Create and get Azure OpenAI deployment value (from Azure OpenAI Studio):
+    1. Create and get Azure OpenAI deployment value (from Azure AI Foundry):
 
-        1. Navigate to [Azure OpenAI Studio](https://oai.azure.com) **from your resource** as described above.
+        1. Navigate to [Azure AI Foundry](https://oai.azure.com) **from your resource** as described above.
         1. Click the **Deployments** tab (sidebar, left) to view currently deployed models.
         1. If your desired model is not deployed, click on **Deploy Model** then select to **Deploy Base Model**.
         1. You will need a chat completion model. For this workshop we recommend using `gpt-4o`. Select `gpt-4o` from the drop down and click **Confirm**.

docs/wksp/07-custom-copilot/index.md

@@ -6,9 +6,8 @@ We will start with by building copilots using Azure AI Studio
 
 Follow this tutorial to build a custom copilot using AI Studio
 
-[Create a project and use the chat playground in Azure AI Studio](https://learn.microsoft.com/en-us/azure/ai-studio/quickstarts/get-started-playground){ .md-button }
-
+[Use the chat playground in Azure AI Foundry portal](https://learn.microsoft.com/en-us/azure/ai-studio/quickstarts/get-started-playground){ .md-button }
 
 ## Step-by Step guide for building a custom copilot using the prompt flow SDK in Azure AI Studio
 
-[Build a custom chat app in Python using the prompt flow SDK](https://learn.microsoft.com/en-us/azure/ai-studio/quickstarts/get-started-code){ .md-button }
+[Build a basic chat app in Python using Azure AI Foundry SDK](https://learn.microsoft.com/en-us/azure/ai-studio/quickstarts/get-started-code){ .md-button }