Optimizing .NET Development Workflow with Cursor AI Rules

Key Points:

• Cursor Composer and Cursor Rules are two features that have significantly saved development time
• The saved time is redirected toward ideation, design, and architecture planning
• Using Cursor AI is compared to “vibe coding” – you’re essentially reviewing the results and checking if they meet requirements

My Take:

This approach to development represents a paradigm shift in how we work. Rather than writing every line of code, developers are becoming orchestrators who define rules and review AI-generated implementations. This can be particularly valuable for projects with well-established patterns like Clean Architecture.

Key Points:

• Rule 1: Generate a technical design document
• Rule 2: Create a breakdown of tasks
• Rule 3: Handle implementation
• These rules formalize the usual workflow when receiving new feature requests

My Take:

Defining these rules is similar to creating documentation for future developers. The difference is that you’re writing it for an AI to understand your project structure and workflow, enabling faster work. It’s like onboarding an AI as another developer on your team.

Key Points:

• The rule requires Cursor to understand the specific request being addressed
• The developer included an overview of their codebase structure in the rules
• Project uses CQRS pattern, Domain-Driven Design, and includes components like auditing
• A structured template for the technical design document is provided
• Rules are updated over time as the project evolves

My Take:

The key insight here is to provide Cursor AI with a clear understanding of your project’s architecture and patterns. The better you can define your system in the rules, the more accurate the generated technical design will be. This is similar to how senior developers create architecture decision records (ADRs) for their teams.

Key Points:

• Tasks are presented in a checklist format
• As Cursor implements each task, it marks it as complete
• Cursor can continue working on incomplete tasks when coming back to the project
• This approach helps ensure no tasks are missed during implementation

My Take:

This structured approach to task management resembles agile methodologies where work is broken down into small, manageable units. The checklist format creates accountability and clear progression tracking, which is essential when delegating work – even to an AI assistant.

Key Points:

• Implementation references both the task document and technical design document
• Rules include requirements for code documentation, comments, and docstrings
• Cursor must update the checklist as tasks are completed
• There’s an experimental feature for commit chains (still being tested)

My Take:

The implementation rule essentially codifies your coding standards and practices. By explicitly defining these expectations, you ensure that AI-generated code maintains the same quality and consistency as manually written code. This is particularly important for maintaining readability and maintainability in larger projects.

Key Points:

• The demonstration uses Cursor Agent with Claude 3.5 (noted as working best for this approach)
• Cursor reviews the task list and identifies completed and pending tasks
• It continues implementation by creating required classes and implementing business logic
• The developer mentions that other models like GPT-3.5 Mini or Claude Opus R1 didn’t work as well

My Take:

The live demonstration showcases the power of well-defined rules and a structured approach. By creating a framework that the AI can follow, complex implementation tasks can be delegated with reasonable confidence. Different AI models may perform differently, so finding the right one for your specific project is important.

Key Points:

• A deep understanding of your project structure is essential for this approach to work effectively
• You need to know what components exist, where they are, and how they work together
• Without proper understanding, letting AI implement features can be risky (compared to “high-dose antibiotics”)
• Short-term gains might lead to long-term technical debt if not managed properly

My Take:

This cautionary note is perhaps the most valuable takeaway from the tutorial. AI-assisted development is a powerful tool, but it requires strong fundamentals and system understanding. It’s not a replacement for developer expertise but rather an amplifier for developers who already know their systems well.