10 Prompts for Cursor: Master AI-Assisted Development in Your IDE

Introduction

Cursor has rapidly become the go-to IDE for developers who want to leverage AI without leaving their editor. Unlike generic AI chat tools, Cursor integrates deeply into the development workflow — offering autocomplete, inline chat, and a powerful command mode that understands your entire codebase. But many developers only scratch the surface, using it as a simple code generator. This article reveals 10 specific prompts — organized by autocomplete, chat, and command mode — that will transform how you work. Each prompt includes a clear use case, an example, and practical tips.

What Makes Cursor Different?

Cursor is a fork of VS Code that embeds AI models (like GPT-4, Claude, and custom models) directly into the editor. Its three core features are:

• Autocomplete: Suggests code completions as you type, similar to GitHub Copilot but with deeper context awareness.
• Chat: A side panel or inline chat where you can ask questions about your code, request refactoring, or generate new code.
• Command Mode (Cmd+K): A keyboard shortcut that lets you give natural language commands to modify the current file or selection.

For the best results, always provide context — include the relevant file, function, or error message in your prompt. Cursor's AI works best when it sees the full picture.

10 Prompts for Cursor

1. Autocomplete: Generate a Complete Function from a Comment

Task: Write a function that fetches and filters data from an API.

Prompt (as a comment in your code):

# function that fetches all users from 'https://api.example.com/users',
# filters by 'active: true', and returns a list of emails

Example output: Cursor will generate the entire function, including error handling and list comprehension:

import requests

def get_active_user_emails():
    try:
        response = requests.get('https://api.example.com/users')
        response.raise_for_status()
        users = response.json()
        active_emails = [user['email'] for user in users if user.get('active')]
        return active_emails
    except requests.RequestException as e:
        print(f"Error fetching users: {e}")
        return []

Tip: Be specific in the comment — include the API endpoint, data structure, and desired output format.

2. Autocomplete: Refactor a Block of Code

Task: Replace a repetitive pattern with a loop.

Prompt: After selecting 10 lines of repetitive code, type a comment:

# replace the above with a for loop that processes items in a list

Example output: Cursor removes the redundant lines and inserts a clean loop.

3. Chat: Explain a Complex Function

Task: Understand a legacy function that nobody in your team remembers.

Prompt in Cursor Chat:

Explain this function step by step. Assume I'm a junior developer. Pay attention to the regex pattern and the recursion.

Example usage: Paste the function into the chat context, and Cursor will break it down, explaining each line, the regex, and the base case.

4. Chat: Generate Unit Tests

Task: Write tests for an existing module.

Prompt:

Generate a comprehensive set of unit tests for the 'user_service' module using pytest. Cover:
- successful user creation
- duplicate email error
- missing required fields
- edge cases (empty name, invalid email format)

Example output: Cursor creates a test file with parametrized tests, fixtures, and assertions.

5. Chat: Debug an Error

Task: Fix a cryptic error message.

Prompt:

I'm getting this error: 'ValueError: invalid literal for int() with base 10: "abc"'
Here's the relevant code:
[Paste your code]
What's wrong and how do I fix it?

Example output: Cursor identifies the line where the error occurs, explains the root cause (e.g., passing a string to int()), and suggests a fix with input validation.

6. Command Mode (Cmd+K): Add a New Feature

Task: Add pagination to an existing endpoint.

Prompt in Cmd+K:

Add pagination support to the /api/users endpoint. Use query parameters 'page' and 'per_page' (default 20). Return a JSON object with 'data', 'total', 'page', and 'per_page'.

Example output: Cursor modifies the current file, adding the pagination logic, parsing query parameters, and updating the response structure.

7. Command Mode: Refactor a Function

Task: Split a large function into smaller, testable units.

Prompt:

Refactor this function into three smaller helper functions: validate_input(), process_data(), and format_output(). Keep the original function as a coordinator.

Example output: Cursor extracts the logic into separate functions, preserving the original signature and adding docstrings.

8. Chat: Generate Documentation

Task: Write docstrings and a README for your project.

Prompt:

Generate Google-style docstrings for all functions in the current file. Then create a README.md that explains the project, installation steps, and usage examples.

Example output: Cursor produces well-formatted docstrings with Args, Returns, and Raises sections, plus a full README.

9. Command Mode: Convert Between Languages

Task: Rewrite a JavaScript function in Python.

Prompt:

Convert this JavaScript function to Python 3. Use type hints and handle any differences in array methods.

Example output: Cursor translates the code, replacing .map() with list comprehension, .filter() with filter(), and adding type hints.

10. Chat: Code Review

Task: Review a pull request without manually scanning every line.

Prompt:

Review this code for:
- security vulnerabilities (especially SQL injection, XSS)
- performance bottlenecks
- code style violations (PEP8)
- missing error handling
Provide a bullet list of issues and suggested fixes.

Example output: Cursor returns a structured review with specific line numbers and actionable recommendations.

Best Practices for Writing Prompts

• Be specific: Instead of "fix this code," say "fix the off-by-one error in the loop at line 23."
• Provide context: Include the file, function, or error message. Cursor uses the open file as context, but adding more helps.
• Set the role: Start with "Act as a senior Python developer" to guide the AI's tone and depth.
• Iterate: If the first result isn't perfect, refine your prompt — add constraints, examples, or formatting instructions.

Common Pitfalls to Avoid

• Vague prompts: "Generate something useful" yields mediocre results. Always specify the task.
• Ignoring context: Cursor's autocomplete works best when you have a few lines of surrounding code. Don't start from an empty file.
• Over-relying on AI: Always review generated code. AI can introduce subtle bugs, security issues, or licensing problems.

Conclusion

Cursor is more than a code autocompleter — it's a pair programmer that can explain, refactor, test, and document your code. By mastering these 10 prompts across autocomplete, chat, and command mode, you'll cut development time dramatically and produce cleaner, more reliable code. Start with one prompt today, and gradually integrate them into your daily workflow. The future of development is AI-assisted, and Cursor is your gateway.

For teams looking to train their AI-assisted development skills systematically, ASI Biont's courses provide structured learning paths that cover Cursor, GitHub Copilot, and other AI tools. The key is consistent practice — try each prompt with your own projects and see the difference.