AI-Assisted Programming: A Complete Practical Workflow from Requirements to Pull Request

As an AI application practitioner, I have witnessed a quiet revolution in the field of programming. In the past, we saw programming as a dance of fingers on the keyboard, a solitary struggle spent staring at screens debugging bugs late into the night. Now, programming is becoming a dialogue between humans and AI.

Many developers have tried AI, but often remain at the beginner stage of "write a quick sort for me" or "help me match an email with regex." Using AI this way is like buying a Ferrari only to drive it around the neighborhood. True AI-assisted programming is a complete workflow ranging from requirement analysis and architectural design to code implementation and finally submitting a PR (Pull Request). It doesn't just generate code; it generates "solutions" for you.

Today, I want to share this proven practical workflow, showing how AI transforms from a simple chatbot into your full-stack technical partner.

Real Pain Points for Developers: Why Are We Always "Tired"?

After communicating with numerous independent developers and small teams, I found that our exhaustion often stems from work outside of "creation":

1. "Translation" Loss of Requirements: You have a brilliant idea in your head, but it becomes fragmented when put into documentation. By the time you write the code, you discover logical holes and have to start over.
2. The Tedium of Boilerplate Code: To implement a simple CRUD (Create, Read, Update, Delete) function, you have to write Router, Controller, Service, DTO, and configure database connections. This mechanical labor devours passion.
3. The Nightmare of Context Switching: After writing code, you have to write unit tests; after tests, comments; and finally, you have to rack your brains for a Commit Message. Every switch consumes cognitive resources.
4. Accumulation of Technical Debt: Due to tight schedules, code is written hastily, lacking exception handling or even logs. When bugs explode, it's too late for regrets.

The essence of these pain points is that we spend too much energy on "low-value repetition," and AI is the best weapon to resolve this contradiction.

What AI Can Do for You: Redefining the Development Flow

In the new workflow, AI's role is no longer a simple "completion tool," but a "co-pilot" spanning the entire lifecycle.

#### 1. Requirements Phase: From "Fuzzy Ideas" to "Technical Specifications"

Many developers don't write documentation not because they are lazy, but because they don't know how to organize their language. AI excels at structured thinking. You just need to throw fuzzy requirements at it, and it can help you sort out feature lists, boundary conditions, and even UI interaction logic.

#### 2. Design Phase: Tech Stack Selection and Architecture Suggestions

Independent developers often wear multiple hats; they might be strong in frontend but weak in backend. AI can recommend the tech stack best suited for the current project based on your requirements and provide directory structure suggestions, avoiding the over-engineering of "introducing a heavy framework for a single feature."

#### 3. Coding Phase: From "Line-by-line Typing" to "Module Generation"

This is the most intuitive efficiency booster. You no longer need to manually write every function. You only need to tell AI: "Implement user registration logic according to the interface just designed, including password encryption and email format validation," and it generates high-quality code snippets. All you do is "review" and "fine-tune."

#### 4. Testing and Submission: Automated Quality Assurance

Writing test cases is a nightmare for many developers, but AI writes test cases quickly and with broad coverage. It can consider edge cases you missed. Finally, it can automatically generate standardized Commit Messages based on code changes, keeping your Git history clean and tidy.

Practical Workflow Demo: Building a "User Points System"

To give you a more intuitive feel, let's use a common feature, the "User Points System," to demonstrate the huge difference before and after AI intervention.

#### Step 1: Requirements Clarification

I told AI: "I want to build a user points system where users can earn points for logging in, commenting, and purchasing goods, and points can be redeemed for coupons."

AI didn't write code immediately but asked back: "Do points have an expiry date? Is the acquisition rate different for users of different levels? How to