Mastering Cursor AI: Four Principles for Scalable, Maintainable Code Generation

Four Principles of Cursor Rules

A letter as if face‑to‑face, sharing practical experiences and reflections on using Cursor AI for code generation.

Common Problems in Real Projects

Rule not applied: The Rules file is correctly configured but the generated code does not follow it.

Token waste: Each request sends the entire Rules file to the model; large files consume unnecessary tokens.

Maintenance difficulty: Splitting Rules into many files often creates redundancy, conflicts, or unreasonable partitions.

Unstable output: Identical rules and similar prompts can produce markedly different code.

Disrupting project style: AI‑generated code varies in style, leading to frequent rejections and re‑prompting.

MSEC Theory

The MSEC (Minimization, Structured, Explicitness, Consistency) theory guides the creation of all Cursor Rules.

01 Minimization Principle

Keep rules short, focused, and easy to understand.

Reasons:

Broad, vague rules are hard for the AI to obey precisely.

Every rule is sent with each request; unnecessary rules waste tokens.

Practical advice:

Keep a rule file under 500 lines.

Split large rules into small, composable units.

Retain only necessary, executable rules; remove ambiguous, redundant, or overly generic ones.

Example: simplify general.mdc to core.mdc, removing duplicated technology‑stack specifications.

02 Structured Principle

A good rule set has clear hierarchy and scope, using layered architecture to manage context and define responsibility boundaries.

Layered rule categories: core.mdc: Project‑wide behavior and style. tech-stack.mdc: Technology stack definitions and official docs. project-structure.mdc: Project layout and file organization.

… (additional layers as needed)

Language‑specific rules (e.g., java.mdc, python.mdc, golang.mdc, typescript.mdc) and framework‑specific rules (e.g., springboot.mdc, django.mdc, android.mdc) are placed in their respective layers.

03 Explicitness Principle

Explicitly tell the model where to look, what to answer, and which source to use, including rule references, code file references, and concrete examples.

Cursor rule file types (MDC format) and their application modes: Always Apply: Always included in the model context. Apply to Specific Files: Automatically included when a file matches the defined glob pattern. Agent Intelligently: AI decides whether to include the rule; a description must be provided. Apply Manual: Included only when explicitly referenced with @ruleName.

Guidelines for using the four modes:

Set general rules to Always Apply so they are always enforced.

Set language rules to Apply to Specific Files so they trigger only for files with the corresponding extension.

Set framework rules to either Apply to Specific Files or Agent Intelligently depending on whether automatic or AI‑driven selection is preferred.

Optional rules (e.g., git.mdc) can use Apply Manual and be invoked with @git.mdc when needed.

Rules can also include other rules or template code. Example of a template rule:

---
description: RPC Service boilerplate
globs:
  alwaysApply: false
---
- Use our internal RPC pattern when defining services
- Always use snake_case for service names.
@service-template.ts

04 Consistency Principle

Maintain uniformity across code style, module structure, naming, process design, and interaction behavior throughout the project.

Recommendations:

Enforce a unified style in core.mdc so AI follows the established conventions from the start.

Define directory and file placement in project-structure.mdc to guide AI where generated code belongs.

Specify language‑specific conventions and framework‑specific best practices in their respective rule files.

All rule files should share a consistent format and style to simplify maintenance and improve AI learning.

Conclusion

If you want AI to assist coding in a medium‑to‑large project, the most important factors are maintainability and readability. A well‑designed rule system ensures AI produces code that fits the project’s architecture, style, and standards, reducing rejections and token waste.

Source code repository: https://github.com/flyeric0212/cursor-rules