Add AI capabilities to your C# application using Semantic Kernel from Microsoft (Part 2)

In the second part of this tutorial, we will apply what we've learned in Part 1 and utilize the Semantic Kernel SDK in a simple C# console app.

Add AI capabilities to your C# application using Semantic Kernel from Microsoft (Part 2)
Semantic Kernel by Microsoft: Adding AI superpowers to your C# app - Part 2
In the second part of this tutorial, we're going to bring Factman to life. If you haven't already, make sure you go through Part 1 then come back to continue reading.

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Using the Semantic Kernel SDK

On my machine, I am running macOS Ventura on an Apple M1 Pro chip, but the same steps should work on any environment.


  • .NET Core / CLI
  • Semantic Kernel SDK
  • OpenAI API Key
  • Visual Studio Code (or your preferred IDE)

Step 1: Create a new C# Console app

In your terminal window type the following: mkdir SKDemo && cd SKDemo. Then, create a new C# console app by typing: dotnet new console.

Now, your SKDemo root directory should look like this:

Folder structure screenshot
Folder structure screenshot

Install the Semantic Kernel SDK package from NuGet by entering the following command:

dotnet add package Microsoft.SemanticKernel --version 1.0.1

At the time of writing version 1.0.1 is the latest stable release. You may choose to install a newer version.

In our Program.cs file, let's prepare a few things:

// Import the Kernel class from the Microsoft.SemanticKernel namespace
using Kernel = Microsoft.SemanticKernel.Kernel;

// Create a new Kernel builder
var builder = Kernel.CreateBuilder();

// Add OpenAI Chat Completion to the builder
Don't forget to replace "OPENAI_API_KEY" with your actual OpenAI API Key. You can get it from the OpenAI website.

We're now ready to start building our Plugins and Functions.

Step 2: Create the Plugins folder

Great, you've made it this far so you're serious about using Semantic Kernel. If you recall from Part 1 of this tutorial, we're going to build two plugins:

  1. FactmanPlugin: Responsible for all myth-related tasks and will include the following functions:
    1. FindMyth: Finds a common myth about AI.
    2. BustMyth: Fact-checks and busts the myth.
    3. AdaptMessage: Adjust the content to align with the specific posting guidelines of a given social media platform.
  2. SocialPlugin: Our second plugin will be used by the Kernel to simulate posting to social media platforms using only one function: Post.

Let's create our Plugins folder, then we'll create another nested folder for our Plugin: FactmanPlugin.

Make sure you're in the root folder SKDemo. Then, in your terminal window type the following:

  • mkdir Plugins && cd Plugins
  • mkdir FactmanPlugin && cd FactmanPlugin
Folder structure screenshot
Folder structure with Plugins and FactmanPlugin folders

Step 3: Create the FindMyth function

Since we will instruct the LLM and ask it to generate a common myth about AI, this function will be of type Prompt.

A quick reminder from Part 1 of this tutorial: Plugins can be Prompts or Native Functions.
  1. Create FindMyth folder: While in the FactmanPlugin directory, type the following: mkdir FindMyth && cd FindMyth

Now, in the FindMyth directory, we'll need two files: skprompt.txt and config.json.

  1. Create skprompt.txt file: This file will contain the natural language prompt for generating AI myths. Create it using the touch command: touch skprompt.txt

The following should go into the skprompt.txt file:




Prompt template: Feel free to play around with this to suit your needs

  1. Create config.json file: This file will be used for our function’s configuration settings. Create it using the touch command: touch config.json.

The following JSON should go into the config.json file:

  "schema": 1,
  "description": "Find a common myth about AI",
  "execution_settings": {
    "default": {
      "max_tokens": 1000,
      "temperature": 0.9,
      "top_p": 0.0,
      "presence_penalty": 0.0,
      "frequency_penalty": 0.0

JSON schema definition of our function