A deep dive into Cursor's rule system, context engineering patterns, and workflows for maintaining quality at speed.
I've been coding with AI since 2020, starting with GitHub Copilot. Five years of figuring out workflows that let me move fast while maintaining quality has taught me a lot about what actually works. The main theme of AI Native DevCon resonates with my experience: we generate a lot of code using these tools, but models hallucinate, they don't always do what you intend, and security issues slip through. That's not useful for production applications.
As a Cursor ambassador for Canada and a senior AI engineer working at a startup in San Francisco, I run AI-assisted coding workshops and speak at conferences about these patterns. Today I want to share the workflows I've developed.
What Makes Cursor Different
Cursor is a VS Code fork, so migration is seamless for anyone already in that ecosystem. But what genuinely differentiates it is how it handles context engineering. I've never seen any other tool do this as efficiently. The agents love to guess things, but if you provide proper context, they don't hallucinate and you get dramatically better output.
Cursor does a lot of context engineering behind the scenes for you. That's the secret sauce. It's not about prompting different models through a nice UI. It's about systematically gathering and providing the right context.
User Rules and Project Rules
User rules are global to your Cursor environment. They apply to all projects and all chat threads. Every single time you send a message, your user rules are included. I use these for consistent preferences: I work a lot with Claude 4.5 Sonnet, and it likes to create markdown files everywhere. I hate it. It keeps telling me I'm "absolutely right" all the time. I hate that too. So those behaviors get banned in my user rules.
Project rules are scoped to specific projects and are far more powerful. They can be invoked via path patterns, manually attached to context, or applied intelligently based on relevance. This "apply intelligently" feature is like Claude's skills. Cursor has had it for over a year. The agent decides when to pull in context based on what you're doing.
What most people don't know: you can create cursor rules in each module of your project. In a monorepo with frontend and backend in the same repository, you can have separate cursor rules for each. Those rules apply specifically to work in their respective modules. This is proper context engineering.
Memory at the Module Level
One powerful pattern: give agents memory of what's inside each module. Every time the agent reads a file or works on a specific module, it consults the memory file for that module. Once done working, it updates that memory autonomously to keep it current. This is one of the most powerful uses of cursor rules in subdirectories.
The memory file describes what the module does, key patterns to follow, decisions that have been made, and pitfalls to avoid. The agent starts each session informed rather than discovering the codebase from scratch.
Security Through Rules
For security, I maintain explicit rules that agents must follow. These aren't suggestions. They're guardrails coded into the rules file. Does the code handle user input safely? Does it use parameterized queries? Does it validate authentication properly?
The agent knows these rules and applies them during generation, not just in review. This front-loads security thinking rather than hoping to catch issues later.
For testing, I explicitly instruct: no fakes, no mocks unless testing external dependencies, no stubs that hide real behavior. Models want to complete tasks and will take shortcuts. They'll fake tests to make things pass. Your rules need to prevent that explicitly: "You are a coding agent. You are able to code. Please code real code. No fakes."
Workflow Structure
The fastest workflow isn't necessarily the best workflow. The best workflow is where you ship confidently. I've found that adding structured verification steps actually increases velocity because you spend less time debugging production issues.
For refactoring legacy code, I work in phases. First, create a detailed plan for a specific module. Create a knowledge transfer file describing what the module does, living at the module level. Create cursor rules for that specific module. Then gradually work through the migration, having models test their own work.
The reason for the knowledge transfer file: it tells the model what the legacy code was doing. When you migrate, the new modules also have files describing what they're supposed to do. When you combine these and ask the model to test whether migration succeeded, it can understand that this was the existing feature and now this is the new implementation.
Give the agent context that you're migrating from one technology to another. It's smart enough to understand that certain issues commonly occur in migrations and will help you address them.
Creating Reusable Workflows
Create workflows as you go. If you're manually prompting every time, you won't get consistent results across the migration. Inconsistent prompting leads to inconsistent codebase, which leads to more mistakes.
I wrap common operations into workflow files that the agent can invoke. Security checks, test generation, code review passes. These become consistent patterns applied uniformly rather than ad-hoc prompts that vary by mood and memory.
Cursor's power comes from treating it as a pair programmer with guardrails, not an autonomous agent you trust blindly. The rules are your guardrails. Invest in them.
Want to see the full workshop with live demos? Watch the complete AI Native DevCon Fall 2025 recording for hours of hands-on Cursor patterns and access to the public GitHub repo with my rules.
For more on AI-native development tools and practices, explore the AI Native Dev Landscape.