How Cursor Rules Transform AI‑Assisted Backend Development

Introduction

In the era of AI‑assisted programming, enabling AI tools to understand project specifications and follow team conventions is key to improving development efficiency. Cursor Rules provides a powerful solution, and this article shares the design thinking behind Cursor Rules in a backend management system, focusing on rule‑type strategies.

Cursor Rules Basic Concept

What Is Cursor Rules?

Cursor Rules defines AI programming behavior through

.cursor/rules/

.mdc

files. These rule files can define project‑specific coding standards, provide development‑process guidance, constrain AI code‑generation behavior, and ensure team development consistency.

Cursor Rule Application Types

Cursor supports four rule‑application levels, each determined by different field combinations.

Always Apply – always included in the model context.

Field configuration: set "alwaysApply": true Application timing: automatically applied to every chat and cmd‑k session

Applicable scenarios: project basic information, core coding standards, technology‑stack constraints

Typical use: core code examples, always loaded into context

Apply to Specific Files – automatically included when a referenced file matches a glob pattern.

Field configuration: set "globs": "pattern" Application timing: applied when a file matches the specified pattern

Applicable scenarios: handling specifications for specific file types (e.g., Go language standards for .go files)

Typical use: automatically applied based on file‑matching patterns

Apply Intelligently – provided to AI, which decides whether to include it based on a description.

Field configuration: set "description": "..." Application timing: AI intelligently applies when the description matches the development context

Applicable scenarios: specific development processes or business‑scenario specifications

Typical use: rules that AI agents request on demand

Apply Manually – included only when the rule name is explicitly referenced with @ruleName.

Field configuration: no rule fields set

Application timing: applied when the user manually mentions the rule

Applicable scenarios: optional tools or auxiliary functions

Typical use: rules manually selected by the user

mdc File Declaration Format

Field Combination Rule Summary

Backend Management Project Cursor Rules System Design

Overall Architecture Design

The backend management system adopts a layered, modular rule‑design architecture.

Rule Classification Design Philosophy

Based on the four Cursor rule types, the backend project’s rule system is designed as three application types.

2.2.1 Always Apply – Basic Constraint Layer

Design goal: Ensure AI always follows the project’s basic agreements and core development patterns.

Included content:

tcf‑general.mdc – project basic information, tech stack, role definitions

tcf‑admin‑code‑style.mdc – coding standards, error handling, logging conventions

workflow/curd/ – standard CRUD development flow (full example)

Reasons for using Always Apply:

Consistency guarantee: all features must follow the basic standards

Role positioning: AI works as an “experienced backend engineer”

Technology‑stack declaration: conveys the tech stack to AI

Coding standards: unified code style, error handling, logging

Typical code example: a complete CRUD chain (api → controller → service → logic → dao) serves as a reference for any requirement

2.2.2 Apply Intelligently – Smart Matching Layer

Design goal: AI reads the description field and intelligently decides whether related specifications are needed, reducing context interference.

Included content:

workflow/belongs‑to‑curd/ – many‑to‑one relationship flow

workflow/has‑many‑curd/ – one‑to‑many relationship flow

api‑client/ – third‑party API wrapper flow

database/ – database design and optimization

auto‑test/ – automated testing standards to let AI verify generated code and self‑repair

Reasons for using Apply Intelligently:

Intelligent recognition: AI decides based on description whether the scenario needs the rule

Reduce interference: only relevant rules are loaded, avoiding unrelated specifications occupying context space

Accurate matching: description enables AI to precisely identify development needs and activate corresponding rules

2.2.3 Apply Manually – Manual Selection Layer

Design goal: Provide optional references and tools that users can manually select according to actual needs.

Included content:

tools/commit/ – commit tool specifications

Reasons for using Apply Manually:

On‑demand use: users manually reference relevant specifications or tools

Tool‑oriented: mainly provides usage methods and operation guidance for specific tools

Avoid automatic interference: rules are applied only when the user explicitly invokes them

Verification Strategy for General‑Purpose Cursor Rules

Since Cursor 1.6, the commands concept has been introduced. Tool‑related content should be placed in the commands directory, while code‑related specifications remain in rules . Previously, tool content in rules could be misinterpreted as plain text rather than executable commands. Migrating tool specifications to commands improves AI understanding and execution accuracy and allows unified inclusion via git submodule for tools like Claude Code or Gemini CLI.

Introducing Neutral‑Scenario Validation

To ensure the rules work universally across different business projects (via git submodule into .cursor/rules), we establish neutral scenarios for testing:

Typical Data‑Structure Validation

Single‑table structure: User management (User) – independent entity with basic and encrypted fields

One‑to‑many structure: Order → OrderItem – master table with multiple child records

Many‑to‑one structure: Employee → Team → Department – three‑level hierarchy showing affiliation

Tree structure: Category – self‑referencing recursive hierarchy

Importance of Neutral‑Scenario Validation

1. Avoid business bias: validating with a specific business project may produce rules that only suit that project, reducing generality.

2. Standardized verification: testing across multiple typical data structures ensures the specifications apply to common business scenarios.

Cross‑Platform Programming Tool Cursor Rules Adaptation Strategy

To give tools like Claude Code and Gemini CLI the same standardized development experience as Cursor, we design three strategies for handling different rule types.

Apply Manually (Manual Application) Adaptation

Scenario: tool‑related specifications such as code generation, testing, commit utilities.

Design idea: similar to Linux aliases; short commands trigger specific rule loading (e.g., /ai-commit).

Implementation: predefined command mappings in the AI tool’s configuration file.

Typical case: when the user types ai-commit, read and execute .cursor/rules/tcf-admin-backend/tools/commit/ai-commit.mdc.

Benefit: simple operation, low memorization cost, improves development efficiency.

Always Apply Adaptation

Scenario: core specifications marked with alwaysApply: true.

Recognition: files containing alwaysApply: true are identified as core rules.

Loading mechanism: on the first message of a session, AI reads all .mdc files with alwaysApply: true from .cursor/rules and adds them to the context.

Command to locate:

find .cursor/rules -name "*.mdc" -exec grep -l "alwaysApply: true" {} \;

Effect: ensures all generated code strictly follows the project’s basic coding standards.

Apply Intelligently Adaptation

Scenario: business specifications applied on demand based on description.

Applicable situation: when it is unclear which specifications are needed, AI recommends intelligently.

Loading mechanism: on the first message, AI reads all .mdc files with a description field, then matches relevance before deciding to load the rule.

Command to gather descriptions: grep -r "description:" .cursor/rules/ --include="*.mdc" Smart advantage: avoids manual selection, precisely matches business‑scenario needs.

Complete Configuration Example (Using CLAUDE.md)

Through these three strategies, tools such as Claude Code and Gemini CLI can reuse Cursor’s rule specifications and achieve the same standardized development experience.

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