How to block Cursor AI from automatically adding console.log statements in production code?

 

Blocking Cursor AI from Adding Console.Log Statements in Production Code

 

When using Cursor AI in software development, it's important to ensure that it does not introduce unwanted console.log statements into production code. Below is a comprehensive guide to help you prevent this from happening by configuring your development environment and AI tool interaction.

 

Understanding Your Current Setup

 

• Identify the version of Cursor AI you are using and see if it has built-in settings for managing console statements.
• Review current project configuration for automated linting and code reviews that may catch unwanted statements.
• Ensure your version control system is properly configured to track changes made by AI tools.

 

Configuring Cursor AI Settings

 

• Check the Cursor AI documentation for any user settings or preferences that offer control over logging behavior.
• If the AI provides a configuration file (e.g., JSON or YAML), look for options specifically related to logging and set them to your desired state (e.g., disable logging).
• In some setups, AI-driven tools might allow annotations or comments in the code to inform behavior; ensure these are documented properly to block `console.log` insertion.

 

Creating a Post-Processing Script

 

• Create a script (JavaScript, bash, or another relevant scripting language) that audits your code base after changes are made by Cursor AI.
• Set up the script to identify and remove `console.log` statements before code is committed to your repository.
• Implement the script execution as part of a pre-commit hook in your version control system to automate this step.

 

Implementing a Linter to Catch Console Outputs

 

• Integrate a JavaScript linter such as ESLint in your development environment to flag unapproved `console.log` statements in production code.
• Configure the ESLint rules to error on `console.log` statements specifically, except in development environments.
• Ensure that the linter runs automatically on file save or during the build process to provide instant feedback.

 

Using Code Reviews and Automation for Validation

 

• Set up a code review process where peers look for unwanted `console.log` instances. Emphasize this during the review if your workflow integrates AI coding solutions.
• Employ automated Code Review tools that might integrate with your AI to check specifically for console usage and other stylistic preferences.

 

Employing Environment-Based Code Execution

 

• Use environment variables to conditionally include or exclude certain code blocks from executing. Restrict `console.log` to only environments marked for debugging.
• Modify `console.log` calls in your development code to check for a development environment flag before executing.
• For instance:

  <pre>
  if (process.env.NODE\_ENV !== 'production') { 
      console.log('Debug information'); 
  }
  </pre>
  

 

Continuous Monitoring and Learning

 

• Regularly review new updates from Cursor AI developers for improved customization options or features that directly control logging behavior.
• Educate development team members on safe practices when using AI-driven code assistants to minimize inadvertent console logging.
• Collect feedback from team experiences and share best practices to better control the AI’s impact on codebase cleanliness.

 

This structured approach helps ensure your production code remains clean from unwanted console logs, leveraging a mixture of configuration, automation, and team processes. Integration and constant iteration are key, given how AI tooling and software development practices both evolve over time.