The Wrong Mental Model
Most developers approach Cursor like a smarter autocomplete. They wait for suggestions, accept or reject them, and measure productivity by how much typing they saved. This is the wrong frame entirely.
Cursor β and AI-assisted coding more broadly β rewards a different kind of thinking. You stop writing code and start describing systems. The implementation becomes secondary to the specification.
The Workflow That Actually Works
After six months of daily use, the workflow that has become second nature is: describe the feature at a high level, let the model produce a draft, then engage in dialogue about what it got wrong. The first draft is almost never correct. The second or third draft, after you have articulated the edge cases, usually is.
The key insight is that the model is better at implementation than at understanding your specific context. You have to provide the context aggressively β what the rest of the codebase looks like, what constraints you are working under, what performance characteristics matter.
Where It Still Falls Down
Debugging remains human territory. The model can suggest fixes, but it cannot feel the difference between a symptom and a root cause. It will confidently patch the symptom and leave the root cause untouched.
Architecture decisions are also still yours. The model will give you a plausible architecture for any problem you describe. Plausible is not the same as right for your specific situation, team, and constraints.