Introduction: AI Is No Longer Just Buzz—It’s a Tool We Can Use
A few years ago, artificial intelligence felt like something reserved for data scientists, academics, or tech giants. Fast forward to today, and AI is right in front of us—in our IDEs, browsers, terminals, and even CLI tools. As a backend developer working mostly with C# and .NET, I’ve seen firsthand how AI can shift the way I write, test, and manage code.
Let me walk you through how I personally use AI in my development workflow, and some specific tools you might find useful—especially if you work in the C# ecosystem.
1. Code Generation and Autocomplete
AI tools I use:
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GitHub Copilot (with Visual Studio)
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IntelliCode in Visual Studio
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ChatGPT (for prototyping and refactoring ideas)
How it helps:
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Copilot generates method bodies, interfaces, and even class constructors.
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IntelliCode enhances IntelliSense by suggesting the most likely method or property based on context.
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GPT helps me break down requirements into snippets, even for niche libraries.
Personal example: While scaffolding DTOs and AutoMapper profiles in a banking API, Copilot handled 80% of the structure based on naming conventions.
2. Debugging and Code Reviews
Tools to try:
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Visual Studio IntelliCode Suggestions
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Copilot (for explaining methods)
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ChatGPT (for logic reviews)
How I use them: I sometimes paste code into ChatGPT with a question like, “Any hidden issues here?” or “Can this LINQ query be improved?” It may not always be right, but it often points out things I overlooked.
Bonus for C# devs: VS now supports inline AI-based quick actions for refactoring and renaming in 2022+ versions with GitHub Copilot integration.
3. Learning and Documentation Summarization
AI is an excellent assistant when dealing with large frameworks or unfamiliar patterns.
Use AI to:
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Summarize ASP.NET Core or EF Core docs.
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Explain reflection, expression trees, or unsafe code in simple terms.
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Quickly break down open-source libraries on NuGet.
Real story: When I was digging into MediatR and FluentValidation for a new project, GPT helped me understand their purpose and interactions faster than any blog post.
4. DevOps and Automation
AI helps me:
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Write GitHub Actions workflows (especially for CI/CD on .NET projects).
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Generate Dockerfile, .yml configurations, and Helm charts.
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Review deployment flow in Azure DevOps pipelines.
Tools I’ve used:
Try this: Ask GPT to write a build pipeline for a .NET 8 Web API with test, build, and deploy stages. It gets surprisingly close.
5. Testing and Quality Assurance
Writing tests used to be my least favorite task. Now?
AI tools that help:
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Copilot (generates test cases and mocks)
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ChatGPT (for writing tests from method names)
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ReSharper + AI plugins (for NUnit/XUnit suggestions)
In my workflow: I describe the function to GPT or Copilot, and I get the full Arrange-Act-Assert scaffold with meaningful variable names.
6. Career Growth & Exploration
AI isn’t just about writing code—it helps grow your developer toolkit:
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Brainstorm SaaS or app ideas.
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Draft blog post outlines or YouTube video scripts.
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Translate a technical solution into business-friendly language.
Personal win: I used GPT to help outline a system design explanation for a tech interview—and landed the job.
Bonus: C#-Friendly AI Tools You Should Try
Here are some tools that work particularly well in the C#/.NET ecosystem:
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GitHub Copilot for Visual Studio – Excellent for real-time code suggestions.
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IntelliCode – Built into Visual Studio; enhances code completion based on your usage.
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ReSharper with AI Assist – Helps with refactoring, renaming, and test generation.
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AWS CodeWhisperer – Has C# support and integrates into VS Code.
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ChatGPT + OpenAI playground – Useful for code reviews and brainstorming.
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Tabnine – Offers C# support with private model training (optional).
Conclusion: AI Is a Force Multiplier for Developers
Whether you're deep in .NET backend systems, dabbling with frontends, or juggling cloud deployments, AI tools have something to offer.
For me, AI isn’t about shortcuts—it’s about clearing the noise so I can focus on architecture, problem-solving, and shipping quality code faster. The key is to treat AI as a collaborator—not a crutch.
So go ahead. Enable Copilot, ask ChatGPT that weird exception question, and see how it fits into your daily workflow. Your future self (and your codebase) will thank you.