AI  

🧠 AI-Powered Documentation Generator for C# Projects

Automate your code documentation and summaries using Generative AI APIs

πŸ’‘ Introduction

Writing clear and detailed documentation is essential for every software project β€” but also one of the most time-consuming parts of development.

Imagine if your code could explain itself β€” generating method summaries, parameter descriptions, and usage notes automatically. That’s where Generative AI-powered documentation tools come in.

In this article, we’ll explore how to build an AI-Powered Documentation Generator for C# projects using .NET 8 and OpenAI GPT API, so you can generate intelligent summaries for your code automatically.

βš™οΈ What Is an AI Documentation Generator?

An AI-powered documentation generator reads your C# classes, methods, and properties, then uses Generative AI to describe them in human-readable form.

For example:
You give the AI this C# method β€”

public int AddNumbers(int a, int b)
{
    return a + b;
}

The AI generates this documentation:

β€œAdds two integer values and returns their sum. Useful for mathematical calculations where integer addition is required.”

🧩 How It Works

  1. Parse C# files to extract code elements

  2. Send code snippets to a Generative AI API

  3. Receive summarized documentation from AI

  4. Write XML or Markdown documentation automatically

🧰 Prerequisites

βœ… .NET 8 SDK
βœ… Visual Studio 2022 or VS Code
βœ… OpenAI API Key
βœ… Basic understanding of C# reflection and file handling

πŸ’» Step-by-Step Implementation

Step 1. Create a Console App

dotnet new console -n AIDocGenerator
cd AIDocGenerator

Step 2. Add Dependencies

dotnet add package OpenAI_API
dotnet add package Newtonsoft.Json

Step 3. Create a Model Class

Models/CodeSnippet.cs

namespace AIDocGenerator.Models
{
    public class CodeSnippet
    {
        public string FileName { get; set; }
        public string MethodCode { get; set; }
    }
}

Step 4. Create AI Service

Services/AiService.cs

using OpenAI_API;
using System.Threading.Tasks;

namespace AIDocGenerator.Services
{
    public class AiService
    {
        private readonly OpenAIAPI _api;

        public AiService(string apiKey)
        {
            _api = new OpenAIAPI(apiKey);
        }

        public async Task<string> GenerateDocumentationAsync(string code)
        {
            string prompt = $"Explain what this C# method does in one paragraph:\n\n{code}";
            var result = await _api.Completions.GetCompletion(prompt);
            return result;
        }
    }
}

Step 5. Read C# Files and Generate Documentation

Program.cs

using AIDocGenerator.Models;
using AIDocGenerator.Services;
using System.IO;
using System.Text.RegularExpressions;

class Program
{
    static async Task Main()
    {
        string projectPath = @"C:\YourProjectFolder"; // Change this path
        string outputFile = "Documentation.md";
        string apiKey = "sk-your-openai-api-key";

        var aiService = new AiService(apiKey);
        var files = Directory.GetFiles(projectPath, "*.cs", SearchOption.AllDirectories);
        var documentation = new List<string>();

        foreach (var file in files)
        {
            string code = File.ReadAllText(file);
            var methods = Regex.Matches(code, @"public\s+\w+\s+\w+\(.*?\)\s*\{[\s\S]*?\}");

            foreach (Match method in methods)
            {
                Console.WriteLine($"Analyzing {file}...");
                var doc = await aiService.GenerateDocumentationAsync(method.Value);
                documentation.Add($"### File: {Path.GetFileName(file)}\n```csharp\n{method.Value}\n```\n**AI Summary:** {doc}\n");
            }
        }

        await File.WriteAllLinesAsync(outputFile, documentation);
        Console.WriteLine($"βœ… Documentation generated: {outputFile}");
    }
}

🧠 Example Output

Generated Documentation.md:

### File: Calculator.cs
```csharp
public int AddNumbers(int a, int b)
{
    return a + b;
}

AI Summary:
This method takes two integers as input and returns their sum. It provides a simple arithmetic operation that can be used in various numeric calculations.


---

## 🧱 Architecture Overview  

C# Source Files
↓
AI Documentation Generator (.NET 8)
↓
OpenAI GPT API
↓
Markdown/XML Output


The tool processes your existing code and automatically generates developer-friendly documentation.

---

## πŸ” Security Best Practices  

βœ… Never hardcode API keys β€” use [User Secrets](https://learn.microsoft.com/en-us/aspnet/core/security/app-secrets)  
βœ… Respect API rate limits  
βœ… Limit file size per request  
βœ… Cache results locally for performance  

---

## ⚑ Real-World Use Cases  

| Use Case | Description |
|-----------|--------------|
| **Enterprise Codebases** | Generate quick documentation for large .NET solutions |
| **Open Source Projects** | Auto-create README or Wiki content |
| **Learning Tools** | Help new developers understand existing code |
| **Code Reviews** | Use AI-summaries for rapid overview of logic |

---

## 🧩 Possible Enhancements  

- Add **XML doc comment insertion** directly above methods  
- Generate **HTML or PDF documentation** automatically  
- Support for **multiple AI models** (OpenAI, Azure OpenAI, Gemini)  
- Integration with **CI/CD pipelines** for automated updates  

---

## 🌍 External References  

- 🧾 [OpenAI API Documentation](https://platform.openai.com/docs/api-reference)  
- πŸ“˜ [Roslyn C# Code Analysis](https://learn.microsoft.com/en-us/dotnet/csharp/roslyn-sdk/)  
- πŸ“— [.NET File I/O Documentation](https://learn.microsoft.com/en-us/dotnet/standard/io/)  
- πŸ’‘ [Azure OpenAI Service](https://learn.microsoft.com/en-us/azure/ai-services/openai/)  
- 🧱 [Markdown Syntax Reference](https://www.markdownguide.org/basic-syntax/)  

---

## 🏁 Conclusion  

AI-powered documentation generation saves developers **hours of repetitive work**, ensuring your projects always have up-to-date, easy-to-read documentation.  

By integrating Generative AI with .NET tools, you can **transform static codebases into self-describing, intelligent systems** β€” perfect for enterprise, education, and open-source use.

The future of documentation is not manual β€” it’s **machine-assisted and AI-driven**. πŸ§ βš™οΈ  

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