Stop hand-coding controllers and unit tests — AI can handle that.
The Rise of AI-Powered Productivity
In traditional development, much of a developer’s time is spent doing one thing: writing boilerplate code.
Controller scaffolds, DTOs, repository patterns, unit test shells, logging wrappers — they’re all necessary, but not exactly innovative.
Today, with tools like GitHub Copilot and ChatGPT, those repetitive pieces can be generated in minutes instead of hours — giving developers and architects more time to focus on business logic and system design.
Architect’s Axiom: If it’s repetitive, predictable, and testable — automate it.
Why Boilerplate Exists in the First Place
Boilerplate isn’t bad — it’s structure.
It enforces consistency and makes systems easier to maintain.
But writing it by hand is unnecessary when:
- The patterns are already well known.
- The rules don’t change from project to project.
- The structure can be described clearly in a prompt or pattern.
AI thrives on this kind of work.
It doesn’t tire, it doesn’t forget naming conventions, and it’s faster than any human typist.
Where AI Excels at Code Generation
AI tools are ideal for automating the scaffolding layer — everything that supports your business layer but doesn’t define it.
Here are the top areas where Copilot and ChatGPT can safely handle the heavy lifting.
1. Data Access and Repository Classes
Most enterprise projects use Entity Framework (EF) or EF Core.
Instead of writing repetitive repository code for every entity, let AI generate it for you.
Example prompt (ChatGPT):
Generate a generic repository pattern for EF Core with async methods for Add, Update, Delete, and GetById.
ChatGPT will produce:
- The full interface (
IRepository<T>) - Implementation class (
Repository<T>) - Async methods with clean exception handling
- Logging integration if requested
From there, you can easily customize or extend.
Architect’s Axiom: AI writes patterns; architects define exceptions.
2. Data Transfer Objects (DTOs)
Once EF Core Power Tools scaffolds your entities, you’ll likely need DTOs for your application or API layer.
Instead of hand-writing 20+ DTOs, ask Copilot to generate them directly from your entity classes.
Example (in Visual Studio with Copilot):
Type public class OrderDto and press Enter — Copilot will auto-complete matching properties from your Order entity.
You can even instruct it:
Generate a DTO for the Order entity that excludes navigation properties and adds an OrderTotal property.
Result: consistent DTOs with no manual effort.
3. Controllers and Endpoints
Controllers often follow the same pattern:
CRUD operations, exception handling, and HTTP response wrapping.
AI can handle this pattern generation with precision.
Example prompt:
Generate an ASP.NET Core controller for the Order entity with CRUD endpoints, using dependency injection and a service layer pattern.
ChatGPT or Copilot will generate:
- Route attributes and methods (GET, POST, PUT, DELETE)
- Dependency injection for the service layer
- Basic validation and response handling
- Optional Swagger annotations
Architect’s Axiom: Stop hand-coding what your AI already understands.
4. Logging and Exception Handling
Instead of pasting try/catch blocks everywhere, let AI apply your chosen logging standard automatically.
Example prompt:
Add Serilog-based exception handling to this service class.
ChatGPT will wrap every method with a try/catch, log errors via Serilog, and optionally rethrow custom exceptions.
This ensures consistency and saves time while adhering to your architectural logging standards.
5. Unit Tests and Integration Tests
AI is surprisingly strong at test scaffolding.
Once your business and data layers exist, prompt Copilot or ChatGPT to write test shells using xUnit or NUnit.
Example prompt:
Generate unit tests for the OrderService class using xUnit. Include mocks for IRepository<Order>.
You’ll get:
- Setup methods
- Mocks (Moq or built-in mocking framework)
- Tests for each method’s expected behavior
- Arrange/Act/Assert structure pre-written
Even if you rewrite half of them, you’ve saved 50–70% of the effort.
Architect’s Axiom: AI can’t replace testing — but it can replace test setup.
Example: A Complete AI-Assisted Workflow
Let’s walk through a quick example using a database-first approach with EF Core Power Tools, Copilot, and ChatGPT:
- Scaffold the Database
- Use EF Core Power Tools to generate entities and
DbContext. - Everything in your Infrastructure layer is ready in minutes.
- Use EF Core Power Tools to generate entities and
- Generate DTOs and Repositories (Copilot)
- Use Copilot to auto-generate DTOs and repository interfaces.
- Customize only where business logic diverges.
- Generate Controllers and Unit Tests (ChatGPT)
- Use ChatGPT to scaffold full controllers and test classes.
- Paste your patterns once, then let AI replicate the structure.
- Apply Consistent Logging and Validation (AI prompt)
- Ask ChatGPT to inject logging and validation logic globally.
- Review and Validate (Human Step)
- Check naming, security, and business correctness.
- Run all tests, fix edge cases, commit with confidence.
Result:
A fully functional, testable API project that once took days — now ready in a few hours.
AI + Automation = Acceleration, Not Replacement
The goal isn’t to remove developers — it’s to remove repetition.
Architects and engineers should spend their time on logic, not loops.
AI handles pattern recognition perfectly. Humans handle meaning, validation, and creativity.
That’s the winning balance.
Architect’s Axiom: Automation accelerates productivity; architecture preserves quality.
Best Practices for AI Code Generation
- Start from strong architecture.
AI performs best when your project structure and naming conventions are clear. - Prompt precisely.
Describe patterns (“repository,” “async,” “DTO”) and your framework (“EF Core,” “xUnit”). - Validate everything.
Never assume AI output is correct. Review, test, and document. - Keep AI-generated code modular.
Don’t bury AI code inside your business layer — keep it at the edges. - Leverage iteration.
Ask AI to refine, not reinvent. Example: “Optimize the repository for bulk inserts.”
Conclusion: The Architect’s New Assistant
AI doesn’t eliminate software craftsmanship — it amplifies it.
It lets architects and developers automate the mechanical and focus on the meaningful.
Your job isn’t to write every line anymore.
It’s to design where lines should be written — and by whom (or what).
Architect’s Axiom: Let automation build. Let AI assist. Let humans design.
🪄 Next in the Series:
“Automation-First Architecture: How to Let AI Build While You Lead.”
We’ll explore how to define the rules, templates, and governance that allow AI to safely automate development — while architects remain firmly in control.
Frequently Asked Questions
What exactly is “boilerplate code”?
Boilerplate code refers to repetitive, structural code that appears in many places across a project — controllers, DTOs, repositories, or test setups.
It’s necessary for consistency and maintainability, but it doesn’t contain unique business logic.
AI tools like Copilot and ChatGPT are perfect for generating this kind of code automatically.
Architect’s Axiom: “If it’s repetitive, predictable, and testable — automate it.”
Is it safe to let AI generate code in production systems?
Yes — with proper oversight.
AI should handle scaffolding (controllers, DTOs, tests, repositories), while humans validate correctness, performance, and security.
Think of AI as your junior developer who types instantly but still needs your architectural direction and review.
What kinds of code should I automate with AI?
AI performs best on:
- Repository and data access layers
- DTOs and mapping logic
- Controllers and endpoints
- Unit and integration test shells
- Logging and exception-handling patterns
It’s less suitable for:
Domain-specific algorithms
Complex business rules
Security-sensitive logic
Performance-critical sections
How do Copilot and ChatGPT differ for code generation?
Copilot is embedded in IDEs like Visual Studio and VS Code. It’s great for in-line suggestions, completing DTOs, methods, or test stubs as you type.
ChatGPT is conversational and better at structural or multi-file generation — controllers, patterns, refactoring, or full architectural examples.
Use both together: Copilot for speed, ChatGPT for structure.
Can AI generate full CRUD APIs in .NET?
Absolutely.
You can prompt ChatGPT or Copilot to generate an entire ASP.NET Core controller with GET, POST, PUT, and DELETE actions, using dependency injection, repository patterns, and logging.
Example prompt:
Generate an ASP.NET Core controller for the Product entity using dependency injection and repository pattern. Include error handling and async methods.
AI will scaffold 80–90% of what you need — you review and refine the rest.
What about tests — can AI write those too?
Yes.
AI can scaffold unit tests, integration tests, and mocks using frameworks like xUnit or NUnit.
Ask it to generate test cases for each method, and you’ll instantly get a structured set of Arrange/Act/Assert tests.
You’ll still review and adjust the edge cases, but it cuts initial effort by 60–80%.
Architect’s Axiom: “AI can’t replace testing — but it can replace test setup.”
How do I make sure AI-generated code follows my architecture standards?
Use prompt-based patterns and governance templates.
For example:
- “Follow clean architecture principles.”
- “Use async naming conventions.”
- “Inject services via constructor dependency injection.”
Once you train ChatGPT or Copilot on your conventions, it will reuse them consistently.
You can also store standard templates (e.g., in snippets or prompt libraries) for repeatable scaffolding.
Can I integrate EF Core Power Tools with AI-generated layers?
Yes — and it’s one of the best workflows.
EF Core Power Tools creates your entities and DbContext automatically.
Then AI builds the DTOs, repositories, controllers, and tests that consume that data layer.
It’s the perfect automation + AI combo for database-first systems.
How does AI affect project timelines and quality?
AI drastically reduces coding time and onboarding friction.
Teams produce more in less time while maintaining consistency.
However, code review and validation remain non-negotiable — that’s where architectural quality is ensured.
AI accelerates production; architects preserve integrity.
What are the main risks of AI-generated code?
The biggest risks are:
- Incorrect assumptions: AI might misunderstand your domain.
- Security oversights: Missing input validation or authorization.
- Performance inefficiencies: Overuse of async, includes, or LINQ.
- Style drift: Inconsistent naming or patterns if prompts vary.
Mitigate these with clear prompts, automated tests, and governance checks.
What’s the best workflow for combining automation and AI?
Here’s the recommended sequence:
Automate with EF Core Power Tools → scaffold data layer.
Generate DTOs, repositories, and controllers using AI.
Inject logging and validation via AI prompts.
Validate with human review and automated tests.
This pattern delivers speed without sacrificing structure.
Architect’s Axiom: “Let automation build. Let AI assist. Let humans review.”
Does AI threaten developer jobs?
No — it transforms them.
AI eliminates repetition, not reasoning.
Developers evolve into problem solvers and validators rather than typists.
Architects evolve into designers of intelligent systems, not just builders.
Architect’s Axiom: “AI replaces repetition — not responsibility.”
Can I trust AI-generated code for enterprise use?
Yes — if you apply governance and validation.
Treat AI output like code from a junior developer: valuable, fast, but review required.
Once validated, it’s just as reliable as any manually written code.
What is the biggest productivity gain from AI automation?
The combination of speed + consistency.
You save time, eliminate typos, and keep patterns uniform across teams.
In enterprise projects, this translates to fewer bugs, faster releases, and more predictable quality.
What’s next after automating boilerplate?
The next evolution is Automation-First Architecture — where AI and automated tools handle setup, scaffolding, and orchestration, and humans focus on business design and governance.
That’s the focus of your next article:
🪄 “Automation-First Architecture: How to Let AI Build While You Lead.”
Want More?
- Check out all of our free blog articles
- Check out all of our free infographics
- We currently have two books published
- Check out our hub for social media links to stay updated on what we publish