AI-Assisted Coding SOP: From Requirements to PR

As an AI application practitioner, I have seen too many developers hold one of two extreme attitudes towards AI-assisted programming: either regarding it as an omnipotent "silver bullet" capable of generating entire systems with a single click, or dismissing it as mere "flashiness" that generates toy code impossible to implement in production.

The truth lies somewhere in between. In independent development and small team collaboration, AI is not a "job stealer" here to replace you in writing code, but rather an "intern" who is tireless, knowledgeable, yet occasionally needs guidance. If you can master this intern, your development efficiency will increase more than 10-fold.

Today, I want to share a set of battle-tested "AI-Assisted Coding SOP" to help you navigate the entire process starting from vague requirements, through design, coding, and testing, to finally completing a PR (Pull Request).

1. The Darkest Hour for Independent Developers: Why Do We Need AI?

Before diving into the process, let's look at the most common pain points for independent developers and small teams. This is not just an issue of efficiency, but the root cause of burnout.

1. "Blank Page Syndrome": Taking over a new project, facing an empty IDE, or confronting a massive and unfamiliar legacy codebase, not knowing where to start. Building the basic architecture often takes days, wearing down initial enthusiasm.
2. The Nightmare of Context Switching: While writing code, you suddenly forget the API parameters of a third-party library. You have to switch to the browser to check the documentation. Ten minutes later when you return, your train of thought is broken.
3. Nobody Wants to Do the "Dirty Work": Writing CRUD interfaces, writing unit tests, writing regular expressions, writing tedious CSS styles. These tasks have low technical content but are extremely time-consuming and prone to errors.
4. The Loneliness of Code Review: Independent developers often work alone ("if one eats, the whole family is fed"). There is no one to Review code after it's written. Logic flaws and security hazards (like SQL injection) are often discovered only after going live.

The emergence of AI is precisely to solve these pain points. It can instantly generate scaffolding code without requiring you to memorize every API detail; it can generate test cases without complaint; and it can act as your strictest Code Reviewer, guarding your code before it is merged.

2. Complete SOP for AI-Assisted Coding: From Requirements to PR

To truly unleash the power of AI, you cannot simply ask "help me write a login function." We need a structured process.

#### Phase 1: Requirements Analysis and Technical Solution Design

Before writing code, the most important thing is to figure out "what to do" and "how to do it."

Traditional Approach: Thinking it through in your head or drawing diagrams on a whiteboard, easily missing edge cases.

AI Approach: Let AI play the role of an Architect.

You can throw vague ideas at AI, letting it help you organize feature points, technical selection, and even output data model designs. For example, if you want to build a "bookkeeping mini-program," AI can quickly break it down into a user module, billing module, and category module, and suggest which database index would be more efficient.

AI's Value: It not only provides a structured line of thought but, more importantly, helps you discover potential logic holes before coding (e.g., if a user cancels their account, how is the billing data handled?).

#### Phase 2: Coding Implementation and Refactoring

This is AI's main battlefield, but you must master the "Divide and Conquer" strategy.

Traditional Approach: Hand-writing every function from scratch, copying and pasting code from old projects, and then patching it up.

AI Approach: Modular Generation + Progressive Iteration.

Do not try to get AI to generate 1000 lines of code at once. You should act like guiding an intern:

1. First, ask AI to generate the data models.
2. Then, ask AI to generate the DAO layer interfaces based on the models.
3. Finally, ask AI to generate the API interfaces.

If the code contains complex algorithms or regex, isolate them for AI to handle. If the generated code style is poor, tell AI directly: "Please refactor this code to conform to SOLID principles and add exception handling."

AI's Value: Reduces coding time from "days" to "hours." You are no longer a typist, but a commander.

#### Phase 3: Testing and Code Review

This step is often overlooked but is key to quality.

Traditional Approach: Manual testing, or simply not writing tests and praying after going live.

AI Approach: Let AI play the role of a Test Engineer.

Throw the written function to AI with the prompt: "Please generate unit tests for this code with as high coverage as possible, including happy paths, exception paths, and edge cases." AI can quickly produce JUnit, PyTest, or Jest test code.

After generating the code, don't rush to submit. Let AI perform a pre-Review: "Please review this code from three dimensions: security, performance, and readability, and provide improvement suggestions."

AI's Value: Fills the gap where independent developers lack teammates for Review, significantly reducing the bug rate.

3. Comparison: Before AI vs. After AI

To illustrate the changes more intuitively, we can compare a specific scenario: Developing a "User Export to Excel Report" feature.

In this comparison, AI not only accelerated typing but, more importantly, raised the ceiling of the code (through best practice suggestions) and protected the floor of the code (through test coverage).

4. Core Weapon: Replicable Prompt Templates

Many developers find AI useless because their prompts are too casual. Here is a "Structured Programming Prompt Template" I summarized through practice. It can significantly improve the precision of AI-generated code.

# Role
You are a senior [language/framework] development expert with 10 years of experience, proficient in design patterns, clean code principles, and high-performance architecture.

# Context
I am developing a [project brief, e.g., e-commerce backend management system].
Current tech stack: [e.g., Python Flask + SQLAlchemy + MySQL]
Current task: [e.g.,