AI handles the “speed,” humans handle the “blueprints.”
This column outlines common software architecture design techniques, aiming to teach you how to tame AI to write industrial-grade, highly maintainable, high-quality code through “architectural design” and “rule constraints (Prompt / Rules),” avoiding the pitfall of “the more code you write, the faster the system crashes.”

Table of Contents

  1. Prologue: The More You Rely on AI to Write Code, the Less You Can Afford to Skip Architecture Design
  2. Preliminary: How to Define Coding Boundaries for AI Using Project Rules

Section 1: Top-Level Architecture Breakdown

  1. Frontend-Backend Separation: Constraining AI’s Division of Labor to Avoid Interface Coupling and Responsibility Confusion
  2. Domain-Driven Design: Drawing Context Boundaries for AI to Say Goodbye to "Big Ball of Mud" Code
  3. Layered Clean Architecture: Standardizing Project Directory Structure to Prevent AI Boundary Violations
  4. Exception and Unified Encapsulation Architecture: Standardizing Unified Return Values, Global Exception Handling, and Parameter Validation

Section 2: Code Decoupling and Interface Specification

  1. Dependency Injection: Forbid AI from Hardcoding Instantiation, Improve Testability and Extensibility
  2. Logging Architecture: Making Every Log Line AI Writes Valuable
  3. RESTful and Contract First: Standardizing Interface Definitions and Unifying API Design Paradigms
  4. Aspect-Oriented Programming: Guiding AI to Extract Cross-Cutting Logic So Logging, Auth, and Instrumentation Are Never Written Twice

Section 3: Security, Storage, and Engineering Infrastructure

  1. Unified Authentication and Authorization: Standardizing AI Token Issuance and Verification Logic
  2. Caching and Multi-Level Storage: Don’t Let AI Blindly Hit the Database
  3. Multi-Data-Source Architecture: Stop Letting AI Lock Your Project to One Database
  4. Centralized Configuration and I18n: Strictly Forbid AI Magic Values and Hardcoded Parameters

Section 4: High-Performance Communication and Expansion Architecture

  1. Async and Concurrency Architecture: Fixing AI’s Synchronous Blocking Patterns to Improve System Throughput
  2. Long-Lived Connections and Streaming Push: Standardizing SSE / WebSocket Implementations to Replace Inefficient Polling
  3. Plugin Architecture and Extension Points: Guiding AI Toward Modular, Pluggable Development for Decoupled, Iterable Features
  1. Architect + AI: The Career Leap from Coder to System Orchestrator

Open source code

🪐 Best of luck 🪐