Cursor for Staff Engineers: Beyond Vibes to Real Productivity

Staff engineers are increasingly encouraged to adopt AI tools, but much of the discussion focuses on basic code generation. This post explores how staff engineers can use Cursor to tackle real engineering challenges and automate tedious tasks that consume valuable time.

A Three-Step Framework for Using Cursor

1. Explore: Share context about the problem and gather relevant information
   • Tell Cursor what files and context it needs
   • Discuss potential solutions and approaches
   • Let Cursor help identify dependencies and edge cases
2. Plan: Break down the chosen solution into manageable steps
   • Create detailed, step-by-step plans
   • Document progress in markdown files
   • Keep track of completed and remaining tasks
3. Build: Execute the plan one step at a time
   • Review each change before implementing
   • Make adjustments as needed
   • Commit changes frequently to Git

Real-World Applications

1. Test-Driven Development

Testing is crucial but often gets deprioritized, especially in startups. Cursor can help write better tests by:

• Analyzing files to identify test coverage needs
• Creating comprehensive test plans
• Implementing tests step by step
• Maintaining context through documentation

This approach works for both new code (TDD) and adding tests to existing codebases.

2. Load Testing and System Analysis

Staff engineers need to understand system constraints and performance characteristics. Cursor helps by:

• Quickly creating load testing scripts
• Testing different performance scenarios
• Collecting and analyzing timing data
• Measuring system behavior under various conditions

Example: When testing a VLM inference engine, Cursor helped create scripts to: – Set up multiple threads – Configure request parameters – Track tokens per second – Monitor performance degradation

3. Handling PR Comments

PR reviews often include many small, tedious changes. Cursor can help by:

• Processing multiple PR comments at once
• Grouping related changes
• Implementing simple fixes automatically
• Maintaining a clear plan for larger changes

Best Practices

1. Use Git Effectively
   • Commit changes frequently
   • Don’t rely on Cursor’s checkpointing
   • Treat Cursor as a pair programming tool
2. Break Down Tasks
   • Give Cursor specific, manageable tasks
   • Review and adjust as needed
   • Don’t expect perfect solutions on the first try
3. Maintain Context
   • Keep planning documents updated
   • Use markdown files to track progress
   • Document decisions and changes
4. Focus on Results
   • Use Cursor to ship features faster
   • Automate repetitive tasks
   • Spend time on high-impact decisions

Common Objections Addressed

1. “I code faster manually because I know the codebase”
   • Deep codebase knowledge helps provide better context to Cursor
   • Consider the actual time spent typing code
   • Focus on making good decisions rather than typing speed
2. “It makes too many mistakes”
   • Break tasks into smaller steps
   • Review changes before committing
   • Use Git to manage changes safely
3. “It only works for new codebases”
   • Cursor works well with existing production code
   • Helps understand and modify legacy systems
   • Useful for both new features and maintenance

Staff engineers should focus on making good system decisions and training other engineers. Cursor can help automate repetitive tasks, allowing more time for these important responsibilities. The key is to use Cursor as a tool to enhance productivity while maintaining control over architectural decisions and code quality.

This post is based on a talk by Vignesh Mohankumar, published on Jason Liu’s channel. Special thanks to both for sharing their insights on practical AI tooling for staff engineers.