How I Built a Low-Code Platform with 90% AI-Generated Code in 2 Months

Background: Others Use Low-Code to Build Apps, I Used AI to Build Low-Code

In an era where everyone talks about AI programming, many use low-code platforms to build business systems. However, existing low-code platforms are either too heavy or too closed—high learning curve, difficult customization, and complex requirements often require writing proprietary SDKs or even modifying source code.

My team needed a controllable, extensible, and pluggable low-code foundation to support multiple industry projects. Traditional manual development would mean:

At least 6 months

3–4 full-stack developers

Large amounts of repetitive wheel-reinvention

So I changed approach: I design the underlying architecture, let AI write 90% of the code, and I review and assemble.

AI Intervention Strategy: Become "Architect + Tamer"

Initially I thought AI could directly write a low-code foundation, but AI doesn't understand business context, won't proactively decompose modules, let alone balance maintainability and performance.

I defined two roles for myself:

Architect

Decide module boundaries (data engine, rendering engine, workflow, plugin mechanism, etc.)

Define interface specifications, data structures, performance metrics

Tamer

Break requirements into small tasks AI can understand

Continuously adjust output quality via prompts

Use multi-turn dialogue to keep AI context consistent

Key point: AI generation quality depends on the task granularity you give and your ability to maintain context. Asking AI to write "a low-code platform foundation" in one shot usually yields a mess.

Core Module AI Generation in Practice

I split the low-code foundation into four core modules, each largely generated by AI then manually refined.

1. Data Modeling Engine

Requirements:

Users define table structures via visual UI

Automatically generate database migration scripts

ORM supporting multiple databases (MySQL, PostgreSQL)

AI Prompt Example (simplified):

用 Node.js + Sequelize 写一个数据建模服务，支持动态创建、更新表结构，
并生成对应的迁移文件，要求兼容 MySQL 和 PostgreSQL，代码要模块化。

AI Output:

Table definition manager

ORM model registrar

Migration generator

Manual Adjustments:

Optimized migration script naming conventions

Added transaction support

Added caching layer to reduce duplicate table-creation requests

2. Page Rendering Engine

Requirements:

Auto-generate forms and tables from JSON Schema

Support component mapping (e.g., input → text box, select → dropdown)

Frontend framework: React

AI Prompt Example:

写一个 React 组件渲染引擎，根据传入的 JSON Schema 渲染表单，
支持 input、select、date、file 等类型，并能动态绑定数据。

AI Output:

Generic FormRenderer component

Schema-to-UI component mapping table

Form validation logic

Manual Adjustments:

Unified form styling standards

Added custom component registration mechanism

3. Workflow Orchestrator

Requirements:

Support visual drag-and-drop nodes

Node types: approval, branch, condition, API call

Execution engine parses BPMN

AI Prompt Example:

用 Node.js 实现一个 BPMN 工作流解析器，
支持审批、条件分支、API 调用三类节点，
要求可扩展新节点类型，执行过程可追踪。

AI Output:

BPMN XML parser module

Node executors (approval, condition, API)

Log tracer

Manual Adjustments:

Added failure retry mechanism

Added timeout settings for API nodes

4. Plugin Mechanism

Requirements:

External developers can write plugins to extend platform functionality

Plugins support hot loading without affecting main process

AI Prompt Example:

用 Node.js 写一个插件管理器，能动态加载 npm 包或本地 JS 文件，
提供生命周期钩子（onLoad、onUnload、onError）。

AI Output:

Plugin loader

Lifecycle hook management

Isolated plugin execution environment

Manual Adjustments:

Added plugin signature verification

Added exception isolation to prevent a single plugin from crashing the whole platform

Secrets to 90% AI Generation Rate

I summarized three practices that make AI a true development workhorse:

Break tasks small enough

Decompose a module into feature points, then further into method-level tasks

Ask AI to write "migration naming rules" instead of "the whole ORM module"

Feed AI with context

Send interface specs first, then ask AI to implement

Ensure AI-generated code style, naming, and structure stay consistent

Generate → Run → Fix

Run unit tests immediately after each code generation

Paste errors back to AI and let it fix itself

Result Comparison

Original Estimate:

6-month cycle

3–4 developers

2 months after launch to stabilize

Actual Outcome:

Completed in 2 months

One person + AI (90% code auto-generated)

Stable within a week of launch, supporting multiple concurrent projects

More importantly, this foundation is now reusable —future industry projects (finance, manufacturing, education, etc.) can build business systems directly on top of it.

Retrospective and Future Plans

Two takeaways from this process:

AI doesn't replace programmers; it turns programmers into "architects"

You don't need to hand-write all code, but you must understand design and quality control

Prompt engineering is the key productivity lever

It's not about whether AI can write code, but whether you can make it write the code you want.

Future iterations will add an AI self-improvement module to the Chengwu low-code platform:

Collect error logs during runtime

Automatically generate fix proposals

Validate in sandbox then hot-update

Then "the platform writing itself" may no longer be a fantasy.

If you're considering building your own software foundation, try the "architect + tamer" role—you might cut your project cycle in half.

Article Summary

90% AI generation rate isn't magic; it's the result of task decomposition + context management

AI excels at writing low-level, repetitive, rule-clear code

Humans should design rules, integrate modules, and ensure quality