Can AI‑Driven DDD Turn Backend Development into an Assembly Line?

Introduction

Developers are stuck in endless cycles of requirement gathering, coding, and bug fixing, and the growing codebase feels like a tangled yarn ball. Recent AI tools such as Copilot, Cursor, and Claude Code have changed coding habits, raising the question of whether the engineering methodology should evolve alongside these tools.

Programming Paradigm Evolution

Three major stages are identified:

Traditional Programming : Developers write code line‑by‑line in an IDE, searching for solutions on Google or StackOverflow.

Intelligent Completion (Tab Era) : AI predicts the next code fragment based on context, offering a "mind‑reading" experience, yet still requires manual acceptance and correction.

Vibe Coding : Developers describe desired outcomes in natural language and AI generates code, dramatically speeding up development but introducing hidden risks such as architectural erosion and uncontrolled coupling.

From Feeling‑Driven to Specification‑Driven

The core idea is to shift from "feelings" to "specifications": before coding, AI and humans must agree on a contract that defines what to build, acceptance criteria, and architectural constraints (DDD layers, coding standards, API contracts).

DDD‑SDD Workflow

The product system at ShouQianBa adopted a five‑stage Spec‑Driven Development process, each with clear inputs, outputs, and acceptance standards.

Stage 1 – Requirement Analysis : Feed the PRD to AI, which acts as a requirements analyst to produce an atomic feature list and a list of boundary exceptions ( 01_requirement_analysis.md).

Stage 2 – Technical Design : AI, constrained by the DDD architecture, outputs a detailed design document and DDL scripts ( 02_technical_design.md, 02_init.sql), e.g., creating a separate unit_default aggregation table instead of patching the existing schema.

Stage 3 – Task Decomposition : AI breaks the solution into 24 independent sub‑tasks following the DDD layer order (Infrastructure → Domain → Application → Interface) and records dependencies ( 03_task_list.md).

Stage 4 – Code Implementation : Guided by code-style.md, AI generates code that adheres to a 12‑step style checklist, automatically checking naming, comments, and null‑pointer risks.

Stage 5 – Code Review : AI runs a self‑review using code-review.md, evaluating architecture compliance, quality, performance, security, standards, and business logic, then produces a report with issues and fixes.

Automation of the Pipeline

To eliminate repetitive PRD parsing, the team built an automated data flow using Feishu MCP to fetch raw documents, a feishu-doc-parser skill to convert them into Markdown ( {project_name}.md), and a dev‑workflow skill integrated into Claude Code. Executing /dev‑workflow loads the appropriate specifications for each stage, preventing context overflow and hallucinations.

Project Case Study: Adding a Default Unit Feature

The workflow was applied to a medium‑size requirement—adding a "default unit" to retail and restaurant products. Challenges included existing unit module integration, multi‑domain interaction, cache invalidation, and preserving architectural layering.

Key outcomes per stage:

Stage 1 produced a precise atomic feature list.

Stage 2 designed a new unit_default aggregation table and updated domain entities (e.g., UnitVO with isDefault).

Stage 3 generated a detailed task list with explicit dependencies (e.g., T22 depends on T21).

Stage 4 implemented code following the 12‑step style guide.

Stage 5 identified two critical bugs (logic error in default name comparison and stale data in unit_default), scoring the overall effort 85/100 (architecture 95, business logic 68).

Reflections

The DDD‑SDD workflow cut the delivery cycle roughly in half, reduced architectural decay, and allowed developers to focus on the 20 % of work that truly requires deep technical insight—complex domain modeling, high‑concurrency design, and performance tuning.

By turning AI from a "wild‑code generator" into a contract‑driven pipeline worker, the team transformed a chaotic coding process into an industrial‑grade production line.