Introduction

In the fast-paced world of software development, efficiency and precision are key. AI-powered tools like Cursor AI can help developers streamline their workflow by automating repetitive tasks, offering intelligent code suggestions, and maintaining project clarity. However, to fully leverage Cursor AI, developers must adopt best practices.

This blog explores 17-18 essential rules for using Cursor AI effectively, with a real-world application: developing a Fake Bill Generator website.

What is Cursor AI?

Cursor AI is an advanced AI-powered code editor that assists developers in writing, editing, and understanding code. It provides contextual suggestions, generates boilerplate code, and improves productivity by automating routine coding tasks.

Visit Cursor AI

Best Practices for Using Cursor AI

1. Define Project-Specific Rules with .cursorrules

Creating a .cursorrules file helps guide AI-generated code by enforcing project-specific conventions.

Example for Fake Bill Generator:

# .cursorrules

- Ensure all generated bills include a unique invoice number.
- Format dates in DD/MM/YYYY format.
- Use placeholder data for names and addresses.

2. Break Down Complex Tasks into Smaller Steps

Instead of prompting Cursor AI for a large function, divide tasks into incremental steps to improve code accuracy.

3. Use UNDERSTANDING.md to Maintain Context

Document the project’s structure, objectives, and workflow for both human developers and AI comprehension.

# UNDERSTANDING.md

## Overview
The Fake Bill Generator creates sample invoices for educational purposes.

## Components
- **Frontend:** HTML/CSS/JavaScript
- **Backend:** FastAPI (Python)
- **Database:** MongoDB (optional for record storage)

## Development Status
- [x] UI completed
- [ ] Backend under development
- [ ] Database integration pending

4. Use Single-Purpose Composers

For different parts of the code (UI, API, business logic), create dedicated Cursor AI instances to prevent context confusion.

5. Manage Context Window Size

Cursor AI has a limited token memory. Avoid overloading it with too much code at once; focus on specific sections.

6. Use Effective Prompting

Writing clear, structured prompts improves Cursor AI’s responses. Instead of:

"Generate a function for invoice creation."

Try:

"Generate a Python function using FastAPI to create an invoice with invoice_id, customer_name, and total_amount."

7. Utilize AI to Explain and Debug Code

If you receive unclear or non-working code, ask Cursor AI to explain it before debugging manually.

# Debug this function:
def calculate_tax(amount):
    return amount * 0.18

8. Structure Code in Reusable Components

Use modular design principles to improve code maintainability.

9. Optimize Performance with AI-Generated Suggestions

Cursor AI can help refactor inefficient code into optimized versions.

10. Validate AI-Suggested Code Before Implementation

Do not blindly accept AI-generated code; always review it for correctness and security.

11. Use AI to Automate Documentation

Cursor AI can generate inline documentation, reducing the time spent on manual commenting.

# Function to generate an invoice
# Parameters: invoice_id, customer_name, total_amount
# Returns: JSON invoice data

12. Create AI-Assisted Unit Tests

Leverage Cursor AI to generate test cases for functions.

def test_calculate_tax():
    assert calculate_tax(100) == 18

13. Leverage AI for Database Queries

Cursor AI can assist in writing optimized SQL queries.

SELECT * FROM invoices WHERE customer_name LIKE '%John%';

14. Use AI to Improve Security Practices

AI can help detect potential security flaws in code, such as SQL injection risks.

15. Track Changes with AI-Assisted Version Control

Integrate AI-powered commit messages into Git workflows.

git commit -m "Refactored invoice generation function to improve performance."

16. Experiment with Different AI Models for Better Results

If a code suggestion isn’t optimal, try different AI-powered approaches within Cursor AI.

17. Integrate AI into Continuous Integration (CI) Pipelines

Automate code analysis and testing with AI-driven CI/CD tools.

18. Stay Updated on Cursor AI Improvements

Regularly check for new features and best practices to optimize your workflow.

Case Study: Developing a Fake Bill Generator

Let’s apply these best practices to develop a Fake Bill Generator website.

Step 1: Set Up Project Rules

Define .cursorrules and UNDERSTANDING.md to maintain clarity.

Step 2: Build the Frontend

Using HTML and JavaScript, create a simple UI for input fields.

<input type="text" id="customer_name" placeholder="Enter Name">
<button onclick="generateBill()">Generate Bill</button>

Step 3: Implement the Backend (FastAPI)

Using Cursor AI, generate a FastAPI route for bill generation.

from fastapi import FastAPI
from datetime import datetime

app = FastAPI()

@app.get("/generate-bill")
def generate_bill(customer_name: str):
    return {
        "invoice_id": "INV" + str(datetime.now().timestamp()),
        "customer_name": customer_name,
        "total_amount": 100.0
    }

Step 4: Store and Retrieve Bills (MongoDB)

Cursor AI helps optimize database queries.

from pymongo import MongoClient
client = MongoClient("mongodb://localhost:27017/")
db = client["billing"]
collection = db["invoices"]

Step 5: Test with AI-Assisted Unit Tests

def test_generate_bill():
    response = generate_bill("John Doe")
    assert "invoice_id" in response

Conclusion

By implementing Cursor AI’s 18 best practices, developers can enhance productivity, maintain cleaner code, and optimize workflow efficiency. These practices are especially useful in real-world projects like the Fake Bill Generator, where AI-driven automation and best coding principles lead to faster development cycles and improved code quality.

Are you ready to leverage Cursor AI in your development process? Try these best practices today and transform your workflow!