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Building Production-Ready AI Features in C# Using OpenAI APIs

Artificial intelligence is no longer limited to research environments. Modern applications increasingly rely on language models for tasks such as text analysis, summarization, decision support, and conversational interfaces. C# and .NET are well-suited for these use cases due to strong typing, dependency injection, asynchronous programming, and mature tooling.

Rather than treating OpenAI as a monolithic intelligence layer, it should be integrated as a bounded external service that assists application logic while remaining fully controlled by deterministic code.

Use Cases for OpenAI in C# Applications

Common production use cases include:

Natural language understanding for user input
Text summarization and classification
Decision support systems
Chat and assistant interfaces
Code or document analysis tools

OpenAI should not replace core business rules, data validation, or security logic.

High-Level Architecture

A recommended architecture separates responsibilities into the following layers:

Application layer orchestrating workflows
AI service layer communicating with OpenAI
Domain logic remaining deterministic
Infrastructure layer handling HTTP, logging, and configuration

This separation ensures testability and prevents vendor lock-in.

OpenAI Service Abstraction

All interaction with OpenAI is encapsulated behind an interface.

public interface IOpenAIService
{
    Task<string> GetCompletionAsync(string prompt);
}

This abstraction allows mocking during testing and future replacement without impacting business logic.

HTTP-Based OpenAI Implementation

The OpenAI API is accessed using HttpClient to avoid dependency instability.

public class OpenAIService : IOpenAIService
{
    private readonly HttpClient _httpClient;
    private readonly string _apiKey;

    public OpenAIService(HttpClient httpClient, IConfiguration configuration)
    {
        _httpClient = httpClient;
        _apiKey = configuration["OpenAI:ApiKey"];
    }

    public async Task<string> GetCompletionAsync(string prompt)
    {
        var requestBody = new
        {
            model = "gpt-4.1-mini",
            messages = new[]
            {
                new { role = "user", content = prompt }
            },
            temperature = 0.2
        };

        var request = new HttpRequestMessage(
            HttpMethod.Post,
            "https://api.openai.com/v1/chat/completions");

        request.Headers.Authorization =
            new System.Net.Http.Headers.AuthenticationHeaderValue("Bearer", _apiKey);

        request.Content = new StringContent(
            System.Text.Json.JsonSerializer.Serialize(requestBody),
            System.Text.Encoding.UTF8,
            "application/json");

        var response = await _httpClient.SendAsync(request);
        response.EnsureSuccessStatusCode();

        var json = await response.Content.ReadAsStringAsync();
        using var doc = System.Text.Json.JsonDocument.Parse(json);

        return doc.RootElement
            .GetProperty("choices")[0]
            .GetProperty("message")
            .GetProperty("content")
            .GetString();
    }
}

Dependency Injection Configuration

services.AddHttpClient<IOpenAIService, OpenAIService>();

The API key is stored securely in environment variables or a secrets manager.

Using OpenAI in Application Logic

The application consumes OpenAI as a supporting service, not as a controller.

public class TextAnalysisService
{
    private readonly IOpenAIService _openAI;

    public TextAnalysisService(IOpenAIService openAI)
    {
        _openAI = openAI;
    }

    public async Task<string> SummarizeAsync(string text)
    {
        var prompt = $"Summarize the following text concisely:\n{text}";
        return await _openAI.GetCompletionAsync(prompt);
    }
}

The output is treated as advisory and validated before use.

Error Handling and Resilience

Production systems must assume external AI services can fail. Recommended practices include:

Timeouts and retry policies
Graceful degradation when AI is unavailable
Strict response validation
Centralized logging of prompts and responses

OpenAI should never be a single point of failure.

Security Considerations

Do not send sensitive or regulated data unless compliance requirements are met.
Never trust model output blindly.
Do not allow AI responses to execute code or modify state directly.
Sanitize prompts and responses.

AI systems must follow the same security standards as any external service.

Performance Considerations

Use asynchronous calls
Cache responses when possible
Avoid large prompts unless required
Batch requests only when supported

Language models introduce latency and should not be used in critical synchronous paths without fallback logic.

Conclusion

OpenAI can be safely and effectively integrated into C# applications when treated as a bounded external reasoning service rather than an autonomous decision maker. By maintaining strict architectural boundaries, leveraging HTTP-based integration, and enforcing validation and observability, developers can build reliable AI-powered systems suitable for real-world production use.

This approach reflects how OpenAI is used in professional software engineering environments today, balancing intelligence with control.

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