Merge pull request #48803 from wwlpublish/f45950ca294d0e1a0783c6be78e8c348475ad2ca4e63a690b80256917b5163e1-live

PMEds28 · web-flow · commit 76914c144325 · 2025-01-23T11:22:13.000Z
Modules/M01-build-your-kernel
diff --git a/learn-pr/wwl-azure/build-your-kernel/1-introduction.yml b/learn-pr/wwl-azure/build-your-kernel/1-introduction.yml
@@ -4,7 +4,7 @@ title: Introduction
 metadata:
   title: Introduction
   description: "Introduction"
-  ms.date: 06/11/2024
+  ms.date: 01/23/2025
   author: wwlpublish
   ms.author: buzahid
   ms.topic: unit
diff --git a/learn-pr/wwl-azure/build-your-kernel/2-what-semantic-kernel.yml b/learn-pr/wwl-azure/build-your-kernel/2-what-semantic-kernel.yml
@@ -4,7 +4,7 @@ title: What is semantic kernel
 metadata:
   title: What is semantic kernel
   description: "What is semantic kernel"
-  ms.date: 06/11/2024
+  ms.date: 01/23/2025
   author: wwlpublish
   ms.author: buzahid
   ms.topic: unit
diff --git a/learn-pr/wwl-azure/build-your-kernel/3-why-use-semantic-kernel.yml b/learn-pr/wwl-azure/build-your-kernel/3-why-use-semantic-kernel.yml
@@ -4,7 +4,7 @@ title: Why use semantic kernel
 metadata:
   title: Why use semantic kernel
   description: "Why use semantic kernel"
-  ms.date: 06/11/2024
+  ms.date: 01/23/2025
   author: wwlpublish
   ms.author: buzahid
   ms.topic: unit
diff --git a/learn-pr/wwl-azure/build-your-kernel/4-how-build-your-kernel.yml b/learn-pr/wwl-azure/build-your-kernel/4-how-build-your-kernel.yml
@@ -4,7 +4,7 @@ title: How to build your kernel
 metadata:
   title: How to build your kernel
   description: "How to build your kernel"
-  ms.date: 06/11/2024
+  ms.date: 01/23/2025
   author: wwlpublish
   ms.author: buzahid
   ms.topic: unit
diff --git a/learn-pr/wwl-azure/build-your-kernel/5-exercise-create-your-endpoint.yml b/learn-pr/wwl-azure/build-your-kernel/5-exercise-create-your-endpoint.yml
@@ -4,7 +4,7 @@ title: Exercise - Create your endpoint
 metadata:
   title: Exercise - Create your endpoint
   description: "Exercise - Create your endpoint"
-  ms.date: 06/11/2024
+  ms.date: 01/23/2025
   author: wwlpublish
   ms.author: buzahid
   ms.topic: unit
diff --git a/learn-pr/wwl-azure/build-your-kernel/6-exercise-build-kernel-object.yml b/learn-pr/wwl-azure/build-your-kernel/6-exercise-build-kernel-object.yml
@@ -4,7 +4,7 @@ title: Exercise - Build a kernel object
 metadata:
   title: Exercise - Build a kernel object
   description: "Exercise - Build a kernel object"
-  ms.date: 06/11/2024
+  ms.date: 01/23/2025
   author: wwlpublish
   ms.author: buzahid
   ms.topic: unit
diff --git a/learn-pr/wwl-azure/build-your-kernel/7-knowledge-check.yml b/learn-pr/wwl-azure/build-your-kernel/7-knowledge-check.yml
@@ -4,7 +4,7 @@ title: Knowledge check
 metadata:
   title: Knowledge check
   description: "Check your knowledge"
-  ms.date: 06/11/2024
+  ms.date: 01/23/2025
   author: wwlpublish
   ms.author: buzahid
   ms.topic: unit
@@ -19,23 +19,12 @@ quiz:
     - content: "Semantic Kernel SDK primarily functions as a standalone AI application."
       isCorrect: false
       explanation: "Incorrect. The Semantic Kernel SDK isn't a standalone AI application."
-    - content: "Semantic Kernel SDK acts as an AI orchestration layer between code and large language models."
-      isCorrect: true
-      explanation: "Correct. Semantic Kernel provides a layer for seamless integration with large language models."
     - content: "Semantic Kernel SDK is exclusively designed for gaming applications."
       isCorrect: false
       explanation: "Incorrect. The Semantic Kernel SDK isn't exclusively designed for gaming applications."
-  - content: "What are connectors in the Semantic Kernel SDK?"
-    choices:
-    - content: "Connectors in the Semantic Kernel SDK refer to electrical components."
-      isCorrect: false
-      explanation: "Incorrect. Connectors in the Semantic Kernel SDK are not electrical components."
-    - content: "Connectors connect the application code to the internet."
-      isCorrect: false
-      explanation: "Incorrect. Connectors in the Semantic Kernel SDK aren't responsible for connecting the application code to the internet."
-    - content: "Connectors serve as the bridge between application code and AI models."
+    - content: "Semantic Kernel SDK acts as a bridge between code and large language models."
       isCorrect: true
-      explanation: "Correct. Connectors facilitate the integration process."
+      explanation: "Correct. Semantic Kernel provides a layer for seamless integration with large language models."
   - content: "Which of the following statements is true about the Semantic Kernel SDK?"
     choices:
     - content: "Semantic Kernel SDK adds unnecessary complexity and developers should avoid it."
@@ -46,4 +35,15 @@ quiz:
       explanation: "Correct. Semantic Kernel SDK eases AI integration by providing abstraction and orchestration."
     - content: "Semantic Kernel SDK is only suitable for advanced AI developers."
       isCorrect: false
-      explanation: "Incorrect. The Semantic Kernel SDK is suitable for developers of all levels."
+      explanation: "Incorrect. The Semantic Kernel SDK is suitable for developers of all levels."
+  - content: "How does the Semantic Kernel handle prompts sent to an AI service?"
+    choices:
+    - content: "It builds the prompt using a provided template and processes the response."
+      isCorrect: true
+      explanation: "Correct. The kernel builds the prompt, sends it to the AI service, receives the response, and parses it before returning it to the application."
+    - content: "It selects multiple AI services simultaneously to run the prompt."
+      isCorrect: false
+      explanation: "Incorrect. The kernel selects the best single AI service to run the prompt."
+    - content: "It bypasses the use of templates to streamline the workflow."
+      isCorrect: false
+      explanation: "Incorrect. The kernel uses provided prompt templates as part of the process."
diff --git a/learn-pr/wwl-azure/build-your-kernel/8-summary.yml b/learn-pr/wwl-azure/build-your-kernel/8-summary.yml
@@ -4,7 +4,7 @@ title: Summary
 metadata:
   title: Summary
   description: "Summary"
-  ms.date: 06/11/2024
+  ms.date: 01/23/2025
   author: wwlpublish
   ms.author: buzahid
   ms.topic: unit
diff --git a/learn-pr/wwl-azure/build-your-kernel/includes/1-introduction.md b/learn-pr/wwl-azure/build-your-kernel/includes/1-introduction.md
@@ -1,4 +1,4 @@
-Semantic Kernel is an open source SDK that allows you to integrate large language models (LLMs) into your own code. Using the Semantic Kernel SDK, you can create intelligent applications that understand and respond to natural language prompts. The possibilities of artificially intelligent (AI) applications are endless, and the Semantic Kernel SDK can help you create AI agents that can automate tasks, provide personalized recommendations, and more.
+Semantic Kernel is an open source SDK that allows you to integrate large language models (LLMs) into your own code. Using the Semantic Kernel SDK, you can create intelligent applications respond and react to natural language prompts. The possibilities of artificially intelligent (AI) applications are endless, and the Semantic Kernel SDK can help you create AI agents that automate tasks, provide personalized recommendations, and more.
 
 Suppose you're a developer for Margie's Travel, a global leader in the travel and hospitality industry. You're tasked with creating a personalized AI travel agent. Rather than creating your own language processing model from scratch, you can use the Semantic Kernel to interface with the language model of your choice and create an AI agent that can:
 
diff --git a/learn-pr/wwl-azure/build-your-kernel/includes/2-what-semantic-kernel.md b/learn-pr/wwl-azure/build-your-kernel/includes/2-what-semantic-kernel.md
@@ -12,22 +12,18 @@ An AI agent is a program that can achieve predetermined goals. AI agents are pow
 
 ## What's Semantic Kernel?
 
-Semantic Kernel integrates LLMs like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. Developers can create "plugins" to interface with the LLMs and perform various tasks. The Semantic Kernel SDK also provides built-in plugins that can quickly enhance an application. Developers can easily utilize LLMs in their own applications without having to learn the intricacies the model's API.
+Semantic Kernel integrates LLMs like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. Using the SDK, developers can create "plugins" to interface with the LLMs and perform various tasks. The Semantic Kernel SDK acts as a bridge between AI capabilities and traditional code, which helps simplify the process of developing AI-powered applications. Developers can easily utilize LLMs in their own applications without having to learn the intricacies the model's API.
 
-:::image type="content" source="../media/semantic-kernel-graphic.png" alt-text="Screenshot of a stack graphic representing the key components of the Semantic Kernel SDK.":::
+The kernel is the central component of the Semantic Kernel. The kernel acts as a dependency injection container that manages all of the services and plugins needed to run your AI application. This provides developers with a centralized location to configure and monitor their AI agents. For example, suppose you invoke a prompt from the kernel. The kernel will perform the following actions:
 
-The key components of the Semantic Kernel SDK are:
+1. Select the best AI service to run the prompt.
+1. Build the prompt using the provided prompt template.
+1. Send the prompt to the AI service.
+1. Receive and parse the response.
+1. Return the response from the LLM to your application.
 
-- AI Orchestration Layer
+Throughout this entire process, you can create events and middleware that are triggered at any of these steps. This means you can perform actions like logging, provide status updates to users, and implement responsible AI.
 
-    The core of the Semantic Kernel stack is an AI orchestration layer that allows the seamless integration of AI models and plugins. This layer is responsible for combining these components to craft innovative user interactions.
+:::image type="content" source="../media/kernel-graphic.png" alt-text="Screenshot of a stack graphic representing the key components of the Semantic Kernel SDK.":::
 
-- Connectors
-
-    The Semantic Kernel SDK offers a set of connectors that enable developers to integrate LLMs into their existing applications. These connectors serve as the bridge between the application code and the AI models.
-
-- Plugins 
-
-    The Semantic Kernel SDK operates on plugins, essentially serving as the "body" of the AI app. Plugins consist of prompts that you want the AI model to respond to and functions that can complete specialized tasks. Developers can use built-in plugins, or create their own. 
-
-For developers eager to delve into AI development, the Semantic Kernel SDK opens the door to creating your own AI agent. The SDK seamlessly integrates models and custom plugins that enable you to create unique and impactful user experiences.
+For developers eager to delve into AI development, the Semantic Kernel SDK opens the door to creating your own AI agent. The SDK seamlessly integrates AI models and custom plugins that enable you to create unique and impactful user experiences.
diff --git a/learn-pr/wwl-azure/build-your-kernel/includes/3-why-use-semantic-kernel.md b/learn-pr/wwl-azure/build-your-kernel/includes/3-why-use-semantic-kernel.md
@@ -1,20 +1,18 @@
-The rapid development of artificial intelligence (AI) capabilities has led to an increased demand for integrating these capabilities into applications. AI tools can offer a myriad of benefits. 
+The rapid development of artificial intelligence (AI) capabilities has led to an increased demand for integrating these capabilities into applications. Using AI agents can offer a myriad of benefits. 
 
 For employees, they can: 
 - Automate repetitive tasks.
 - Provide relevant insights. 
 - Boost efficiency and productivity.
 - And more! 
 
-For customers, copilots can:
+For customers, they can:
 - Streamline processes.
 - Offer personalized recommendations.
 - Provide 24/7 availability. 
 - And more!
 
-However, the process of integrating AI into applications can be complex and time-consuming, requiring developers to learn different APIs and navigate the intricacies of each service. The Semantic Kernel SDK solves that problem by providing a layer of abstraction, allowing developers to focus on creating great features instead.
-
-Enterprise solutions can benefit from implementing the Semantic Kernel SDK in the following ways:
+However, the process of integrating AI into applications can be complex and time-consuming, requiring developers to learn different APIs and navigate the intricacies of each service. The Semantic Kernel SDK solves that problem by providing a layer of abstraction, allowing developers to focus on creating great features instead. Enterprise solutions can benefit from implementing the Semantic Kernel SDK in the following ways:
 
 - Streamlined Integration 
 
diff --git a/learn-pr/wwl-azure/build-your-kernel/includes/4-how-build-your-kernel.md b/learn-pr/wwl-azure/build-your-kernel/includes/4-how-build-your-kernel.md
@@ -4,7 +4,7 @@ The steps to get started using the Semantic Kernel SDK are:
 
 1. Install the Semantic Kernel SDK.
 
-    In Visual Studio Code, you can use the command: `dotnet add package Microsoft.SemanticKernel --version 1.2.0`
+    In Visual Studio Code, you can use the command: `dotnet add package Microsoft.SemanticKernel --version 1.30.0`
 
 2. Navigate to the Azure portal.
 
@@ -19,15 +19,16 @@ The steps to get started using the Semantic Kernel SDK are:
     ```c#
     using Microsoft.SemanticKernel;
 
-    // Create kernel
-    var builder = Kernel.CreateBuilder();
-    builder.AddAzureOpenAIChatCompletion(
-        deploymentName: "[The name of your deployment]",
-        endpoint: "[Your Azure endpoint]",
-        apiKey: "[Your Azure OpenAI API key]",
-        modelId: "[The name of the model]" // optional
-    );
-    var kernel = builder.Build();
+    // Populate values from your OpenAI deployment
+    var modelId = "";
+    var endpoint = "";
+    var apiKey = "";
+
+    // Create a kernel with Azure OpenAI chat completion
+    var builder = Kernel.CreateBuilder().AddAzureOpenAIChatCompletion(modelId, endpoint, apiKey);
+
+    // Build the kernel
+    Kernel kernel = builder.Build();
     ```
 
 In the following exercises, you can practice setting up your own semantic kernel project.
diff --git a/learn-pr/wwl-azure/build-your-kernel/includes/5-exercise-create-your-endpoint.md b/learn-pr/wwl-azure/build-your-kernel/includes/5-exercise-create-your-endpoint.md
@@ -11,24 +11,23 @@ For this exercise, you create an Azure OpenAI resource and provide the deploymen
 
 1. After the resource is created, select **Go to resource**.
 
-1. On the **Overview** page, select **Go to Azure OpenAI Studio**.
+1. Under **Resource Management**, select **Keys and Endpoint**.
 
-:::image type="content" source="../media/model-deployments.png" alt-text="A screenshot of the Azure OpenAI deployments page.":::
+    You use the data here in the next exercises to build your kernel. Remember to keep your keys private and secure!
 
-1. Select **Create New Deployment** then **Deploy Model**.
+1. On the **Overview** page, select **Go to Azure OpenAI Studio**.
 
-1. Under **Select a model**, select **gpt-35-turbo-16k**.
+    A new window should open with the Azure AI webpage.
 
-    Use the default Model version
+1. Under **Shared resources** select **Deployments**.
 
-1. Enter a name for your deployment.
+1. Select **Deploy Model** then select **Deploy base model**.
 
-1. When the deployment completes, navigate back to your Azure OpenAI resource.
+1. Select the **gpt-35-turbo-16k** model and select **Confirm**.
 
-1. Under **Resource Management**, go to **Keys and Endpoint**.
+1. Select **Deploy**.
 
-    You'll use the data here in the next exercises to build your kernel
-    Remember to keep your keys private and secure!
+    The model deployment details should appear. Now you're ready to begin building your AI application!
 
 > [!IMPORTANT]
-> Be sure not to delete this resource until you've completed this learning path. You'll need it to complete the exercises.
+> Be sure not to delete this resource until you've completed this learning path. You need it to complete the exercises.
diff --git a/learn-pr/wwl-azure/build-your-kernel/includes/6-exercise-build-kernel-object.md b/learn-pr/wwl-azure/build-your-kernel/includes/6-exercise-build-kernel-object.md
@@ -1,7 +1,8 @@
 In this exercise, you learn how to build your first Semantic Kernel SDK project. You create a new project, add the Semantic Kernel SDK NuGet package, create a kernel object, and run your first prompt. Let's get started!
 
 > [!IMPORTANT]
-> You must have [Visual Studio 2022](https://www.visualstudio.com) or Visual Studio Code with the [C# Dev Kit extension](https://code.visualstudio.com/docs/csharp/get-started) and [.NET 8.0](https://dot.net/download) installed to complete these steps.
+> You must have Visual Studio Code and the .NET Framework 8.0 installed to complete these steps. 
+> You may also need to install the Visual Studio Code C# Dev Kit extension.
 
 1. Open Visual Studio Code and create a new project.
 
@@ -13,30 +14,32 @@ In this exercise, you learn how to build your first Semantic Kernel SDK project.
 
 3. In the Terminal, run the following command to install the Semantic Kernel SDK:
 
-    `dotnet add package Microsoft.SemanticKernel --version 1.2.0`
+    `dotnet add package Microsoft.SemanticKernel --version 1.30.0`
 
 4. Enter the following code to create the kernel:
 
     ```c#
     using Microsoft.SemanticKernel;
 
-    var builder = Kernel.CreateBuilder();
-    builder.AddAzureOpenAIChatCompletion(
-        "your-deployment-name",
-        "your-endpoint",
-        "your-api-key",
-        "deployment-model");
-    var kernel = builder.Build();
+    // Populate values from your OpenAI deployment
+    var modelId = "gpt-35-turbo-16k";
+    var endpoint = "";
+    var apiKey = "";
+
+    // Create a kernel with Azure OpenAI chat completion
+    var builder = Kernel.CreateBuilder().AddAzureOpenAIChatCompletion(modelId, endpoint, apiKey);
+
+    // Build the kernel
+    Kernel kernel = builder.Build();
     ```
 
-    Be sure to replace the placeholders with the values from your Azure resource.
+    Use the endpoint and key values from your Azure OpenAI resource deployment.
 
 5. To test that your kernel and endpoint is working, enter the following code:
 
     ```c#
-    var result = await kernel.InvokePromptAsync(
-            "Give me a list of breakfast foods with eggs and cheese");
-        Console.WriteLine(result);
+    var result = await kernel.InvokePromptAsync("Give me a list of breakfast foods with eggs and cheese");
+    Console.WriteLine(result);
     ```
 
 6. Run the code and check that you see a response similar to the following:
diff --git a/learn-pr/wwl-azure/build-your-kernel/index.yml b/learn-pr/wwl-azure/build-your-kernel/index.yml
@@ -2,26 +2,32 @@
 uid: learn.wwl.build-your-kernel
 metadata:
   title: Build your kernel
-  description: Learn how the semantic kernel SDK connects native code to large language models to create smart applications.
-  ms.date: 06/11/2024
+  description: "Learn how the semantic kernel SDK connects native code to large language models to create smart applications."
+  ms.date: 01/23/2025
   author: wwlpublish
   ms.author: buzahid
   ms.topic: module
+  ms.collection: N/A
   ms.custom:
   - N/A
+  ms.service: semantic-kernel
   ms.devlang: csharp
   ai-usage: human-only
-  ms.service: semantic-kernel
 title: Build your kernel
 summary: This module introduces the Semantic Kernel SDK. Learn how the kernel connects code to large language models to extend functionality with generative artificial intelligence.
 abstract: |
   -   Understand the purpose of Semantic Kernel.
+
   -   Understand prompting basics.
+
   -   Learn techniques for more effective prompts.
 prerequisites: |
   -   Experience programming in C#.
+
   -   Visual Studio Code IDE installed.
+
   -   Familiarity with Azure and the Azure portal.
+
   -   Access to Azure OpenAI services.
 iconUrl: /training/achievements/generic-badge.svg
 levels:
@@ -42,4 +48,4 @@ units:
 - learn.wwl.build-your-kernel.knowledge-check
 - learn.wwl.build-your-kernel.summary
 badge:
-  uid: learn.wwl.build-your-kernel.badge
+  uid: learn.wwl.build-your-kernel.badge
diff --git a/learn-pr/wwl-azure/build-your-kernel/media/kernel-graphic.png b/learn-pr/wwl-azure/build-your-kernel/media/kernel-graphic.png
diff --git a/learn-pr/wwl-azure/build-your-kernel/media/model-deployments.png b/learn-pr/wwl-azure/build-your-kernel/media/model-deployments.png
diff --git a/learn-pr/wwl-azure/build-your-kernel/media/semantic-kernel-graphic.png b/learn-pr/wwl-azure/build-your-kernel/media/semantic-kernel-graphic.png

Original file line number	Diff line number	Diff line change
`@@ -1,4 +1,4 @@`
`1`		`-Semantic Kernel is an open source SDK that allows you to integrate large language models (LLMs) into your own code. Using the Semantic Kernel SDK, you can create intelligent applications that understand and respond to natural language prompts. The possibilities of artificially intelligent (AI) applications are endless, and the Semantic Kernel SDK can help you create AI agents that can automate tasks, provide personalized recommendations, and more.`
	`1`	`+Semantic Kernel is an open source SDK that allows you to integrate large language models (LLMs) into your own code. Using the Semantic Kernel SDK, you can create intelligent applications respond and react to natural language prompts. The possibilities of artificially intelligent (AI) applications are endless, and the Semantic Kernel SDK can help you create AI agents that automate tasks, provide personalized recommendations, and more.`
`2`	`2`
`3`	`3`	`Suppose you're a developer for Margie's Travel, a global leader in the travel and hospitality industry. You're tasked with creating a personalized AI travel agent. Rather than creating your own language processing model from scratch, you can use the Semantic Kernel to interface with the language model of your choice and create an AI agent that can:`
`4`	`4`