How AI Can Accelerate Backend Development: Practical Workflow, Prompts, and Real‑World Cases

Artificial intelligence has been a hot topic for over three years, and this article focuses on its practical application in backend development rather than theory.

Practice Scenario: Backend development using AI coding tools such as Tongyi Lingma and Claude‑4‑Sonnet.

AI Coding Workflow: From interface definition → API documentation → schema generation → persistence layer → business logic layer → implementation, the process can be fully automated and reproduced.

Materials Preparation: Technical solution templates, engineering structure prompts, and rule files (Agent prompts, Copilot Rules, User Rules, Project Rules) are prepared to guide AI output.

Layered Decomposition: Each sub‑section consists of Agent prompts, Prompt Rules, and AI output screenshots, allowing direct copy‑and‑paste with minimal adjustments.

Engineering Structure: A detailed tree of modules (admin‑service, admin‑dal, admin‑client, etc.) is generated using prompts, illustrating a classic three‑layer architecture with clear directory organization.

Code Generation Tasks: The article provides concrete prompts for generating interface definitions, implementation code, database schemas, MyBatis mappers, and domain services, emphasizing strict adherence to design rules, SOLID principles, Lombok annotations, and comprehensive JavaDoc comments.

Practical Reflections: Discusses challenges such as AI hallucination, the need for deterministic specifications, and the importance of balancing certainty with flexibility in the development process.

Production Cases: Three real projects are presented—low‑order‑price conversion, retail service‑package recommendation, and reverse payment reminder—showing AI‑generated code contributions of 50‑70% and highlighting the workflow’s impact on efficiency.

Future Outlook: Suggests extending AI assistance to architecture design, technical proposals, and PRD planning, while emphasizing the shift for developers toward higher‑level tasks like business understanding and system design.