Using Cursor AI in Your Development Workflow

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$ Elevate Your Workflow with Cursor or Windsurf

Learn how to integrate AI-powered editors into your daily development workflow, boosting productivity and streamlining your coding processes.

You've already set up Cursor AI and Windsurf AI, and now it's time to weave them into your daily development workflow. These AI-powered editors aren't just tools—they're your coding sidekicks, ready to boost productivity, streamline processes, and make your life easier. In this tutorial, we'll explore daily usage tips, strategies for reviewing AI-generated code, integration with other development tools like Git, deployment pipelines, peer reviews, and automated testing, plus best practices for working with AI assistants.

workflow_overview.md

→ Daily Usage Tips Get practical advice for incorporating AI tools into your everyday coding routine for both Cursor AI and Windsurf AI.
→ Reviewing AI-Generated Code Learn strategies to effectively review, test, and refine code generated by AI tools.
→ Integration with Development Tools Discover how to connect AI editors with Git, project management tools, CI/CD pipelines, and testing.
→ Best Practices Follow expert recommendations to get the most out of your AI coding assistants.
→ Practical Examples See a day-in-the-life workflow scenario combining multiple AI features.

Daily Usage Tips

cursor_ai_tips.md

→ AI Chat for Instant Help
Use the AI Chat panel to ask, "Why is this loop not working?" or "Explain this function." It's like having a coding tutor at your fingertips.
→ Generate Code from Comments
Type # Create a function to add two numbers, and watch Cursor AI whip up the code. It's a game-changer for speeding up development.
→ Custom Rules for Consistency
Define standards in .cursorrules, like "Use 4 spaces for indentation," to ensure the AI sticks to your style.
→ AI-Generated Commit Messages
Before committing, type # Generate commit message, and Cursor AI suggests a descriptive message based on changes, keeping your Git history clean.

windsurf_ai_tips.md

→ Cascade for Complex Tasks
Use Cascade for multi-file projects, like implementing a function in math_ops.py and calling it from main.py. Describe it, and let Cascade handle the rest.
→ Supercomplete for Smart Suggestions
Get context-aware completions that fit your entire project, making coding flow seamlessly.
→ Memories System for Continuity
The Memories system remembers your previous chats, so your AI interactions feel continuous, perfect for long sessions.
→ Manual Commit Messages
Write them yourself in the Git panel, but ask the AI for suggestions to craft meaningful messages.

Reviewing AI-Generated Code

AI can generate code fast, but it's not perfect. Here's how to ensure quality:

Test with Sample Inputs: Run AI-generated functions with various inputs, including edge cases, to catch bugs early.
Check for Errors: Ensure error handling is robust, especially for critical parts, like database queries.
Verify Standards: Make sure the code follows your project's rules, like using snake_case for variables.
Understand the Code: Take time to read through; you'll need to maintain it later, so know how it works.
Iterate and Refine: If it's off, refine your instructions or tweak the code. For example, if a function misses an edge case, ask the AI to add it.

Pro Tip: Always review AI-generated code as if it was written by a team member. Look for logical errors, security issues, and performance bottlenecks. The AI is a powerful assistant, but you're still responsible for the quality of the final code.

Integration with Other Development Tools

Let's see how Cursor AI and Windsurf AI play nice with your workflow:

Version Control Systems (Git)

Both have built-in Git support: stage files, commit, and resolve conflicts right in the editor.
Cursor AI Bonus: Use # Generate commit message for AI-crafted messages, saving time and keeping history clear.
Windsurf AI: Write manual messages, but ask the AI for suggestions to make them descriptive.

Project Management Tools

No direct integration, but copy task descriptions from Jira or Trello into comments, like # Implement user login feature as per task XYZ-123, and let the AI generate code.
Link issues in comments for traceability, enhancing collaboration.

Deployment Pipelines

Use AI to generate or maintain scripts for CI/CD tools like Jenkins or GitHub Actions. For example, describe # Create a GitHub Actions workflow for CI, and the AI can draft the YAML file.
Keep scripts updated by asking the AI to suggest improvements, ensuring smooth deployments.

Automated Testing

Generate test cases by describing the function, like # Write unit tests for a function that adds two numbers. The AI can create tests compatible with PyTest or Jest.
Ensure tests cover edge cases and integrate with your testing framework, saving time on manual test writing.

Peer Review Processes

Use AI to pre-check code before reviews. Ask, "Are there any security issues in this code?" to catch vulnerabilities early.
During reviews, use AI Chat to explain code to peers, like "Explain how this function handles errors," making reviews more efficient.
Share AI-generated suggestions with reviewers to streamline feedback, reducing back-and-forth.

Best Practices for Working with AI Assistants

ai_best_practices.md

→ Be Specific
Clear instructions get better results. Instead of "Fix this," say, "Fix this loop to handle empty lists."
→ Provide Context
For complex tasks, include relevant code or project details. For example, mention, "This is part of a web API, ensure it handles HTTP errors."
→ Review and Iterate
Always check AI outputs and refine them. If a generated test misses a case, ask, "Add a test for negative numbers."
→ Use Custom Rules
Define rules in .cursorrules or .windsurfrules to guide the AI, like "Use parameterized queries for database operations."
→ Stay Updated
Check Cursor AI Documentation and Windsurf AI Documentation for new features.
→ Critical Thinking
Don't rely solely on AI for critical code; verify, especially for security-sensitive parts.

Practical Example: A Day in Your Workflow

Imagine you're working on a new feature for a web app. Here's how your workflow might look:

Start with Cursor AI, use AI Chat to brainstorm, "How should I structure this user authentication feature?"
Generate code with # Create a login function with JWT authentication.
Review it, test with sample inputs, and ensure it follows your security rules.
Commit with an AI-generated message, "Add JWT authentication for user login."
Switch to Windsurf AI for a multi-file task, use Cascade to implement the feature across auth.py and routes.py, describing, "Implement login in auth.py and call it from routes.py."
Review the output, integrate with your GitHub Actions CI/CD by generating a workflow file.
Share with peers for review, using AI to explain, "Explain how this handles expired tokens."
Run automated tests generated by the AI, like # Write tests for login function, and deploy after passing CI.

This workflow saves hours and keeps code tight.

Daily Usage Tips Reference

Tool	Feature	Tip	Example
Cursor AI	AI Chat	Ask questions for instant help	"Why is this function not working?"
Cursor AI	Code Generation from Comments	Use comments to generate code	Type "# Create a function to add two numbers."
Windsurf AI	Cascade	Use for multi-file tasks	"Implement function in math_ops.py, call from main.py."
Windsurf AI	Supercomplete	Get context-aware completions	Accept suggestions for function calls

Integrating with CI/CD Pipelines

The Big Picture
Imagine your CI/CD pipeline, the backbone of your software deployment, getting a boost from AI. Cursor AI and Windsurf AI, your trusty coding sidekicks, can help generate code, tests, and scripts that make your pipeline faster and more reliable.

ci_cd_integration.md

→ Use AI for Code Generation
Within Cursor AI or Windsurf AI, generate code for new features or fixes. For example, in Cursor AI, type # Create a function to add two numbers and review the output. Commit this to your repository, and your CI pipeline will build it.
→ Generate Test Cases with AI
Ask the AI to create unit tests, like "Generate unit tests for this function" in Cursor AI's AI Chat, or use Windsurf AI's Cascade for multi-file test generation. These tests run in your CI pipeline, ensuring thorough validation.
→ Optimize Deployment Scripts
Use the AI to draft or refine deployment scripts, such as # Create a GitHub Actions workflow for CI in Cursor AI. Review, commit, and let your CD pipeline handle the deployment.
→ Leverage AI for Code Review
Before committing, use AI Chat to review code for errors or improvements, ensuring high quality before it hits the pipeline, reducing build failures.

Why It Works

Both tools are editor-based, so they don't have direct APIs for pipeline integration, but their outputs are still significant. Cursor AI can generate commit messages, keeping your Git history clean, while Windsurf AI's Cascade handles complex, multi-file tasks, saving time in development that benefits the pipeline. It's like prepping your code for success before it even enters the pipeline.

Practical Tip: Test AI-generated code and scripts locally before committing to avoid pipeline hiccups. Use extensions like PyTest for running tests, ensuring compatibility with your pipeline.