AI Coding Workflow: Practical Guide to Boosting Development Efficiency

Beliefs

The author adopts an AI‑First mindset: always ask AI first, treat AI as a teammate that can eventually perform any human‑doable task, and aim to minimize manual intervention.

Basic workflow reconstruction

Three levels of restructuring are described:

Low‑level: individual developers purchase AI coding tools to cut development time.

Mid‑level: the company purchases tools and mandates time compression.

Standard: the whole team adopts AI tools without making them a performance metric, improving anxiety for each role.

Specific role‑based practices include:

Product managers generate PRDs with cursor and a PRD checklist.

Designers improve design efficiency.

Front‑end developers accelerate UI and logic implementation.

Back‑end developers speed up coding.

Testers enhance regression efficiency.

Ops staff streamline deployment and online operations.

Practical tips

Use AI to generate PRDs directly from the code repository.

Define reusable Rule: PRD creation task and store results in a shared workspace.

When AI fails, refine the prompt, add missing knowledge, and let the AI summarize a new rule.

Deep reconstruction

The author proposes vertical and horizontal compression of roles and processes, arguing that each step should present the least difficulty to AI while humans only handle tasks AI cannot.

Implementation details

The end‑to‑end pipeline uses the following components:

Claude code (or similar) on a server, consuming tokens.

A CI/CD pipeline (CloudEff pipeline) triggered via API.

A requirement‑management platform (CloudEff project collaboration) for bugs and features.

A code‑hosting platform (CloudEff Codeup) that can trigger downstream actions.

A containerized deployment platform for front‑end previews.

Key commands:

./node_modules/.bin/claude -p "帮我修复一下bug：$BUG_DESCRIPTION 
 $DESC 

修复完成后，请总结一下你做了哪些具体的修改，并自动提交代码。" --output-format stream-json --verbose --permission-mode acceptEdits

After AI generates a fix, the workflow creates a merge request via CloudEff API, deploys the code, generates a preview link, and shares it with the team. The pipeline is best run on a dedicated machine to avoid token‑consumption delays.

Human intervention

Humans verify bug fixes, advance the pipeline (approve/reject), and may skip self‑testing if confidence is high. AI can also assist in MR review, automated testing, and rule generation.

AI strengths and weaknesses

Strengths (as listed by ChatGPT) include rapid generation of common code structures, CRUD operations, form handling, routing, state management, front‑end component scaffolding, back‑end API scaffolding, documentation parsing, code refactoring, unit‑test generation, cross‑language translation, and debugging assistance.

Weaknesses include handling complex business logic with insufficient context, multi‑module coordination, deep performance or security optimizations, rare or proprietary tech stacks, and reasoning with incomplete information.

Summary

AI coding serves as an excellent code assistant but not as an architect or project manager. Paired with experienced developers, it can dramatically improve productivity, though human oversight remains essential for complex projects.