AI Apps for .NET Developers

https://learn.microsoft.com/en-us/dotnet/ai/
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### Install MCP Server Templates

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/build-mcp-server?source=recommendations

Installs the required project templates for creating MCP servers. Requires .NET 10.0 SDK or later.

```bash
dotnet new install Microsoft.McpServer.ProjectTemplates
```

--------------------------------

### Start Ollama server

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/chat-local-model?source=recommendations

Execute this command to start the Ollama server. If it's already running, it will display a list of available commands.

```Bash
ollama serve

```

--------------------------------

### Install the MCP C# SDK

Source: https://learn.microsoft.com/en-us/dotnet/ai/get-started-mcp

Use the .NET CLI to add the ModelContextProtocol package to your project.

```bash
dotnet add package ModelContextProtocol --prerelease
```

--------------------------------

### Start Ollama Server

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/chat-local-model

Execute this command to start the Ollama server. If the server is already running, it will display a list of available commands.

```Bash
ollama serve
```

--------------------------------

### Run phi3:mini model

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/chat-local-model?source=recommendations

Start the 'phi3:mini' model after it has been downloaded. This will provide a prompt for interaction.

```Bash
ollama run phi3:mini

```

--------------------------------

### Install .NET Evaluation Console Tool

Source: https://learn.microsoft.com/en-us/dotnet/ai/evaluation/evaluate-with-reporting

Installs the local .NET tool for AI evaluation. You might need to create a manifest file first.

```bash
dotnet tool install --local Microsoft.Extensions.AI.Evaluation.Console
```

--------------------------------

### Chain of thought prompting with examples in C#

Source: https://learn.microsoft.com/en-us/dotnet/ai/conceptual/chain-of-thought-prompting?source=recommendations

Provide examples to guide the model on how to break down tasks step-by-step. The model will interpret these examples and apply the same structure to its output. This method is useful for demonstrating complex output formats.

```C#
prompt= """
        Instructions: Compare the pros and cons of EVs and petroleum-fueled vehicles.

        Differences between EVs and petroleum-fueled vehicles:
        - 

        Differences ordered according to overall impact, highest-impact first: 
        1. 
        
        Summary of vehicle type differences as pros and cons:
        Pros of EVs
        1.
        Pros of petroleum-fueled vehicles
        1. 
        """
```

--------------------------------

### Install MCP Server Template

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/build-mcp-server

Installs the MCP Server project templates. .NET 10.0 SDK or later is required.

```bash
dotnet new install Microsoft.McpServer.ProjectTemplates

```

--------------------------------

### Install .NET AI App Template

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/ai-templates?pivots=github-models&tabs=visual-studio%2Cconfigure-visual-studio%2Cconfigure-visual-studio-aspire

Install the AI Chat Web App template package from NuGet using the `dotnet new install` command. This makes the template available for creating new projects.

```bash
dotnet new install Microsoft.Extensions.AI.Templates
```

--------------------------------

### Install OpenAI packages

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/build-chat-app?pivots=openai

Install the necessary NuGet packages for OpenAI integration, including the OpenAI client, AI abstractions, and configuration management.

```bash
dotnet add package OpenAI
dotnet add package Microsoft.Extensions.AI.OpenAI --prerelease
dotnet add package Microsoft.Extensions.Configuration
dotnet add package Microsoft.Extensions.Configuration.UserSecrets
```

--------------------------------

### Install Local .NET Tool

Source: https://learn.microsoft.com/en-us/dotnet/ai/evaluation/evaluate-with-reporting?source=recommendations

Installs the Microsoft.Extensions.AI.Evaluation.Console .NET tool locally. You might need to create a manifest file first.

```bash
dotnet tool install --local Microsoft.Extensions.AI.Evaluation.Console

```

--------------------------------

### Install the in-memory vector store provider

Source: https://learn.microsoft.com/en-us/dotnet/ai/vector-stores/how-to/use-vector-stores

Use the .NET CLI to add the in-memory provider package for development and testing purposes.

```bash
dotnet package add Microsoft.SemanticKernel.Connectors.InMemory --prerelease
```

--------------------------------

### Install Azure.AI.OpenAI NuGet package

Source: https://learn.microsoft.com/en-us/dotnet/ai/how-to/content-filtering

Commands to add the required Azure OpenAI client library to your .NET project.

```bash
dotnet add package Azure.AI.OpenAI
```

```bash
dotnet package add Azure.AI.OpenAI
```

--------------------------------

### Install Required NuGet Packages

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/text-to-image

Add the necessary .NET Aspire and Azure OpenAI packages to your project.

```bash
dotnet add package Aspire.Azure.AI.OpenAI --prerelease
dotnet add package Azure.AI.OpenAI
dotnet add package Microsoft.Extensions.AI.OpenAI --prerelease
```

--------------------------------

### Install Microsoft.ML.Tokenizers packages

Source: https://learn.microsoft.com/en-us/dotnet/ai/how-to/use-tokenizers

Use the .NET CLI to add the core library and the specific data package for Tiktoken models.

```bash
dotnet add package Microsoft.ML.Tokenizers
```

```bash
dotnet add package Microsoft.ML.Tokenizers.Data.O200kBase
```

--------------------------------

### Install required NuGet packages

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/use-function-calling?source=recommendations

Adds the necessary Microsoft.Extensions.AI and configuration packages to the project.

```bash
dotnet add package Azure.Identity
dotnet add package Azure.AI.OpenAI
dotnet add package Microsoft.Extensions.AI
dotnet add package Microsoft.Extensions.AI.OpenAI
dotnet add package Microsoft.Extensions.Configuration
dotnet add package Microsoft.Extensions.Configuration.UserSecrets
```

```bash
dotnet add package Microsoft.Extensions.AI
dotnet add package Microsoft.Extensions.AI.OpenAI
dotnet add package Microsoft.Extensions.Configuration
dotnet add package Microsoft.Extensions.Configuration.UserSecrets
```

--------------------------------

### Install Azure OpenAI packages

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/build-chat-app?pivots=openai

Install the necessary NuGet packages for Azure OpenAI integration, including identity, Azure OpenAI client, AI abstractions, and configuration management.

```bash
dotnet add package Azure.Identity
dotnet add package Azure.AI.OpenAI
dotnet add package Microsoft.Extensions.AI.OpenAI --prerelease
dotnet add package Microsoft.Extensions.Configuration
dotnet add package Microsoft.Extensions.Configuration.UserSecrets
```

--------------------------------

### Add OpenAI SDK package

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/create-assistant

Install the OpenAI NuGet package to the project.

```bash
dotnet add package OpenAI
```

--------------------------------

### Install required NuGet packages

Source: https://learn.microsoft.com/en-us/dotnet/ai/evaluation/evaluate-safety?source=recommendations

Adds the necessary Microsoft.Extensions.AI and Azure SDK packages to the project.

```bash
dotnet add package Azure.AI.OpenAI
dotnet add package Azure.Identity
dotnet add package Microsoft.Extensions.AI.Abstractions
dotnet add package Microsoft.Extensions.AI.Evaluation
dotnet add package Microsoft.Extensions.AI.Evaluation.Reporting
dotnet add package Microsoft.Extensions.AI.Evaluation.Safety --prerelease
dotnet add package Microsoft.Extensions.AI.OpenAI --prerelease
dotnet add package Microsoft.Extensions.Configuration
dotnet add package Microsoft.Extensions.Configuration.UserSecrets
```

--------------------------------

### Run phi3:mini Model

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/chat-local-model

Start the `phi3:mini` model using Ollama. This will provide a prompt for you to begin interacting with the AI.

```Bash
ollama run phi3:mini
```

--------------------------------

### Install required NuGet packages

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/build-chat-app?source=recommendations

Add the necessary dependencies for Azure or OpenAI integration and configuration management.

```bash
dotnet add package Azure.Identity
dotnet add package Azure.AI.OpenAI
dotnet add package Microsoft.Extensions.AI.OpenAI --prerelease
dotnet add package Microsoft.Extensions.Configuration
dotnet add package Microsoft.Extensions.Configuration.UserSecrets
```

```bash
dotnet add package OpenAI
dotnet add package Microsoft.Extensions.AI.OpenAI --prerelease
dotnet add package Microsoft.Extensions.Configuration
dotnet add package Microsoft.Extensions.Configuration.UserSecrets
```

--------------------------------

### Install Azure OpenAI packages

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/generate-images?pivots=openai

Add the necessary NuGet packages for Azure OpenAI integration, including configuration management.

```bash
dotnet add package Azure.AI.OpenAI
dotnet add package Azure.Identity
dotnet add package Microsoft.Extensions.Configuration
dotnet add package Microsoft.Extensions.Configuration.UserSecrets
```

--------------------------------

### Few-shot prompt example

Source: https://learn.microsoft.com/en-us/dotnet/ai/conceptual/zero-shot-learning

A few-shot prompt that provides examples of user input and expected intent completions to guide the model's response.

```C#
prompt = $"""
Instructions: What is the intent of this request?
If you don't know the intent, don't guess; instead respond with "Unknown".
Choices: SendEmail, SendMessage, CompleteTask, CreateDocument, Unknown.

User Input: Can you send a very quick approval to the marketing team?
Intent: SendMessage

User Input: Can you send the full update to the marketing team?
Intent: SendEmail

User Input: {request}
Intent:
""";
```

--------------------------------

### Initialize Project from Template

Source: https://learn.microsoft.com/en-us/dotnet/ai/get-started-app-chat-template?tabs=github-codespaces

Clone the specified GitHub repository template into the local environment.

```bash
azd init -t azure-search-openai-demo-csharp
```

--------------------------------

### Use chain of thought prompting in examples

Source: https://learn.microsoft.com/en-us/dotnet/ai/conceptual/chain-of-thought-prompting

Provide a template or example structure within the prompt to guide the model on how to break down tasks and format its output.

```C#
prompt= """
        Instructions: Compare the pros and cons of EVs and petroleum-fueled vehicles.

        Differences between EVs and petroleum-fueled vehicles:
        - 

        Differences ordered according to overall impact, highest-impact first: 
        1. 
        
        Summary of vehicle type differences as pros and cons:
        Pros of EVs
        1.
        Pros of petroleum-fueled vehicles
        1. 
        """;
```

--------------------------------

### Initialize Local Project Directory

Source: https://learn.microsoft.com/en-us/dotnet/ai/get-started-app-chat-template?tabs=github-codespaces

Create and enter a new directory for the project on your local machine.

```bash
mkdir my-intelligent-app && cd my-intelligent-app
```

--------------------------------

### Create and navigate to a .NET console project

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/build-chat-app?source=recommendations

Initialize a new console application and move into the project directory.

```bash
dotnet new console -o ChatAppAI
```

```bash
cd ChatAppAI
```

--------------------------------

### Example of FunctionInvokingChatClient Usage in C#

Source: https://learn.microsoft.com/en-us/dotnet/ai/how-to/access-data-in-functions

This snippet demonstrates how to initialize FunctionInvokingChatClient, define an AIFunction to retrieve weather information using additional properties from ChatOptions, and make a call to get a response.

```csharp
FunctionInvokingChatClient client = new FunctionInvokingChatClient(
    new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(apiKey))
    .GetChatClient(model).AsIChatClient());

AIFunction getWeather = AIFunctionFactory.Create(() =>
    {
        // Access named parameters from the arguments dictionary.
        AdditionalPropertiesDictionary props = 
            FunctionInvokingChatClient.CurrentContext.Options.AdditionalProperties;

        string location = props["location"].ToString();
        string units = props["units"].ToString();

        return $"Weather in {location}: 35°{units}";
    });

var chatOptions = new ChatOptions
{
    Tools = [getWeather],
    AdditionalProperties = new AdditionalPropertiesDictionary {
        ["location"] = "Seattle",
        ["units"] = "F"
    },
};

List<ChatMessage> chatHistory = [
    new(ChatRole.System, "You're a helpful weather assistant.")
];
chatHistory.Add(new ChatMessage(ChatRole.User, "What's the weather like?"));

ChatResponse response = await client.GetResponseAsync(chatHistory, chatOptions);
Console.WriteLine($"Response: {response.Text}");

```

--------------------------------

### Navigate to Project Directory

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/build-mcp-server

Changes the current directory to the newly created SampleMcpServer project folder.

```bash
cd SampleMcpServer

```

--------------------------------

### Initialize a .NET console application

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/create-assistant

Create a new console project in an empty directory.

```bash
dotnet new console -o AIAssistant
```

--------------------------------

### Clone GitHub Repository for Azure Data Retrieval Augmented Generation Samples

Source: https://learn.microsoft.com/en-us/dotnet/ai/vector-stores/tutorial-vector-search

Clone the sample application repository from GitHub to get started with the Azure Data Retrieval Augmented Generation Samples.

```bash
git clone https://github.com/microsoft/AzureDataRetrievalAugmentedGenerationSamples.git

```

--------------------------------

### Inject and Use IChatClient in Endpoint

Source: https://learn.microsoft.com/en-us/dotnet/ai/how-to/app-service-aoai-auth?pivots=azure-portal

Inject the registered IChatClient into your application's endpoints to make requests to the Azure OpenAI service. This example shows a simple GET endpoint for testing prompts.

```csharp
app.MapGet("/test-prompt", async (IChatClient chatClient) =>
{
    return await chatClient.GetResponseAsync("Test prompt", new ChatOptions());
})
.WithName("Test prompt");

```

--------------------------------

### Initialize .NET Console Project

Source: https://learn.microsoft.com/en-us/dotnet/ai/vector-stores/how-to/build-vector-search-app

Creates a new console application directory and navigates into it.

```bash
dotnet new console -o VectorDataAI
```

```bash
cd VectorDataAI
```

--------------------------------

### Install .NET AI App Template

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/ai-templates?pivots=github-models&tabs=visual-studio%2Cconfigure-visual-studio%2Cconfigure-visual-studio-aspire

Installs the AI Chat Web App template package from NuGet. Ensure you have the .NET 9.0 SDK installed.

```.NET CLI
dotnet new install Microsoft.Extensions.AI.Templates

```

--------------------------------

### Example Search Query Output

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/process-data

An example of user input for searching the processed documents.

```text
Enter your question (or 'exit' to quit): What is data ingestion?

```

--------------------------------

### Create and open the server.json file

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/publish-mcp-registry

Commands to navigate to the project directory and open the manifest file in Visual Studio Code.

```powershell
cd path\my\project

# create and open the server.json file
code .mcp\server.json
```

```bash
cd path/my/project

# create and open the server.json file
code .mcp/server.json
```

--------------------------------

### Create MCP Server App with .NET CLI

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/build-mcp-server

Creates a new MCP Server app named SampleMcpServer. Use --help for more template options.

```bash
dotnet new mcpserver -n SampleMcpServer

```

--------------------------------

### Clone the sample repository

Source: https://learn.microsoft.com/en-us/dotnet/ai/tutorials/tutorial-ai-vector-search

Use this command to download the sample application code from GitHub.

```bash
git clone https://github.com/microsoft/AzureDataRetrievalAugmentedGenerationSamples.git
```

--------------------------------

### MCP Server Response Example

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/build-mcp-server?source=recommendations

This is an example of a successful response from the MCP server after it has been invoked with a prompt and provided with necessary inputs.

```text
The weather in Redmond is balmy.

```

--------------------------------

### Create a new .NET console application

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/generate-images

Initializes a new console project in the current directory.

```bash
dotnet new console -o ImagesAI
```

--------------------------------

### Verify Ollama Installation

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/chat-local-model

Run this command in your terminal to check if Ollama is installed and accessible on your device. If available, it will display a list of commands.

```Bash
ollama
```

--------------------------------

### Create and navigate to project directory

Source: https://learn.microsoft.com/en-us/dotnet/ai/get-started-app-chat-template

Use these commands to create a new local directory for your project and then navigate into it. This is part of the local Visual Studio Code setup.

```bash
mkdir my-intelligent-app && cd my-intelligent-app

```

--------------------------------

### Initialize a vector store instance

Source: https://learn.microsoft.com/en-us/dotnet/ai/vector-stores/how-to/use-vector-stores

Create an instance of the vector store implementation; replace the in-memory provider with a production-ready connector as needed.

```csharp
// Create an in-memory vector store (no external service required).
// For production, replace this with a connector for your preferred database.
var vectorStore = new InMemoryVectorStore();
```

--------------------------------

### Initialize project with Azure Developer CLI

Source: https://learn.microsoft.com/en-us/dotnet/ai/get-started-app-chat-template

Clone the specified GitHub repository to your local computer using the Azure Developer CLI. This command initializes your project with the necessary template.

```bash
azd init -t azure-search-openai-demo-csharp

```

--------------------------------

### Create and Build an MCP Server Project

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/build-mcp-server?source=recommendations

Commands to scaffold a new MCP server project and build it from the command line.

```bash
dotnet new mcpserver -n SampleMcpServer
```

```bash
cd SampleMcpServer
```

```bash
dotnet build
```

--------------------------------

### Zero-shot prompt example

Source: https://learn.microsoft.com/en-us/dotnet/ai/conceptual/zero-shot-learning

A zero-shot prompt that asks the model to classify user input into specific intents without providing prior examples.

```C#
prompt = $"""
Instructions: What is the intent of this request?
If you don't know the intent, don't guess; instead respond with "Unknown".
Choices: SendEmail, SendMessage, CompleteTask, CreateDocument, Unknown.
User Input: {request}
Intent: 
""";
```

--------------------------------

### Install Azure OpenAI packages

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/prompt-model?source=recommendations

Install the necessary NuGet packages for Azure OpenAI integration, including Azure AI OpenAI, Azure Identity, and AI extensions for OpenAI.

```Bash
dotnet add package Azure.AI.OpenAI
dotnet add package Azure.Identity
dotnet add package Microsoft.Extensions.AI.OpenAI --prerelease
dotnet add package Microsoft.Extensions.Configuration
dotnet add package Microsoft.Extensions.Configuration.UserSecrets
```

--------------------------------

### Example AI Response to '.NET facts' prompt

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/chat-local-model?source=recommendations

This is an example output from the AI model providing three facts about .NET in response to a user prompt. It showcases the initial detailed response from the AI.

```text
Your prompt:
Tell me three facts about .NET.

AI response:
1. **Cross-Platform Development:** One of the significant strengths of .NET,
particularly its newer iterations (.NET Core and .NET 5+), is cross-platform support.
It allows developers to build applications that run on Windows, Linux, macOS,
and various other operating systems seamlessly, enhancing flexibility and
reducing barriers for a wider range of users.

2. **Rich Ecosystem and Library Support:** .NET has a rich ecosystem,
comprising an extensive collection of libraries (such as those provided by the
official NuGet Package Manager), tools, and services. This allows developers
to work on web applications (.NET for desktop apps and ASP.NET Core
for modern web applications), mobile applications (.NET MAUI),
IoT solutions, AI/ML projects, and much more with a vast array of prebuilt
components available at their disposal.

3. **Type Safety:** .NET operates under the Common Language Infrastructure (CLI)
model and employs managed code for executing applications. This approach inherently
offers strong type safety checks which help in preventing many runtime errors that
are common in languages like C/C++. It also enables features such as garbage collection,
thus relieving developers from manual memory management. These characteristics enhance
the reliability of .NET-developed software and improve productivity by catching
issues early during development.


```

--------------------------------

### Example AI Response to .NET Facts Prompt

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/chat-local-model

This is an example output from the AI model providing three facts about .NET in response to a user's prompt. It showcases the initial detailed response from the AI.

```text
Your prompt:
Tell me three facts about .NET.

AI response:
1. **Cross-Platform Development:** One of the significant strengths of .NET, 
particularly its newer iterations (.NET Core and .NET 5+), is cross-platform support.
It allows developers to build applications that run on Windows, Linux, macOS, 
and various other operating systems seamlessly, enhancing flexibility and 
reducing barriers for a wider range of users.

2. **Rich Ecosystem and Library Support:** .NET has a rich ecosystem, 
comprising an extensive collection of libraries (such as those provided by the 
official NuGet Package Manager), tools, and services. This allows developers 
to work on web applications (.NET for desktop apps and ASP.NET Core 
for modern web applications), mobile applications (.NET MAUI), 
IoT solutions, AI/ML projects, and much more with a vast array of prebuilt 
components available at their disposal.

3. **Type Safety:** .NET operates under the Common Language Infrastructure (CLI) 
model and employs managed code for executing applications. This approach inherently 
offers strong type safety checks which help in preventing many runtime errors that 
are common in languages like C/C++. It also enables features such as garbage collection, 
thus relieving developers from manual memory management. These characteristics enhance 
the reliability of .NET-developed software and improve productivity by catching 
issues early during development.


```

--------------------------------

### Zero-shot learning prompt example in C#

Source: https://learn.microsoft.com/en-us/dotnet/ai/conceptual/zero-shot-learning?source=recommendations

Use this C# code to create a zero-shot prompt. It instructs the model to determine user intent from a given request without providing explicit examples of completions.

```csharp
prompt = $"""
Instructions: What is the intent of this request?
If you don't know the intent, don't guess; instead respond with "Unknown".
Choices: SendEmail, SendMessage, CompleteTask, CreateDocument, Unknown.
User Input: {request}
Intent: 
"""
```

--------------------------------

### Initialize .NET Console Application

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/quickstart-ai-chat-with-data

Creates a new console project in the current directory.

```bash
dotnet new console -o VectorDataAI
```

--------------------------------

### Configure Assistant with File Search and Code Interpreter

Source: https://learn.microsoft.com/en-us/dotnet/ai/quickstarts/create-assistant?source=recommendations

Set up assistant options, enabling file search and code interpreter tools. This configuration includes defining the assistant's name, instructions, and tool resources, specifically a vector store linked to the uploaded file.

```C#
// Configure the assistant options
AssistantCreationOptions assistantOptions = new() 
{
    Name = "Example: Contoso sales RAG",
    Instructions =
        "You are an assistant that looks up sales data and helps visualize the information based" 
        + " on user queries. When asked to generate a graph, chart, or other visualization, use" 
        + " the code interpreter tool to do so.",
    Tools = 
    {
        new FileSearchToolDefinition(), // Enable the assistant to search and access files
        new CodeInterpreterToolDefinition(), // Enable the assistant to run code for data analysis
    },
    ToolResources = new()
    {
        FileSearch = new()
        {
            NewVectorStores =
            {
                new VectorStoreCreationHelper([salesFile.Id]),
            }
        }
    },
};

```

--------------------------------

### Query the chat application

Source: https://learn.microsoft.com/en-us/dotnet/ai/get-started-app-chat-template

Example query to test follow-up question suggestions.

```text
What is included in my Northwind Health Plus plan that is not in standard?
```

--------------------------------

### Initialize Reporting Configuration

Source: https://learn.microsoft.com/en-us/dotnet/ai/evaluation/evaluate-with-reporting

Sets up disk-based reporting with a unique execution name and scenario tracking.

```C#
private string ScenarioName => $"{TestContext!.FullyQualifiedTestClassName}.{TestContext.TestName}";

private static string ExecutionName => $"{DateTime.Now:yyyyMMddTHHmmss}";

private static readonly ReportingConfiguration s_defaultReportingConfiguration =
    DiskBasedReportingConfiguration.Create(
        storageRootPath: "C:\\TestReports",
        evaluators: GetEvaluators(),
        chatConfiguration: GetAzureOpenAIChatConfiguration(),
        enableResponseCaching: true,
        executionName: ExecutionName);
```

--------------------------------

### Create an MSTest project

Source: https://learn.microsoft.com/en-us/dotnet/ai/evaluation/evaluate-safety?source=recommendations

Initializes a new MSTest project directory using the .NET CLI.

```bash
dotnet new mstest -o EvaluateResponseSafety
```
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