Code generation
Turn requirements into working code in context.
Generate with the codebase in context
The advantage of generating code in Cursor is that it can see your project. Reference the relevant files, lean on your indexing, and let your rules carry your conventions, and the generated code slots into what already exists instead of standing apart from it. This is the difference from a general chatbot: you are not describing your codebase from memory, you are pointing the AI at the real thing. Give it the requirement plus the surrounding context, and the output fits.
Point at the right files
Cursor generates best when you steer its attention. Mention the modules the new code will touch, the patterns it should follow, and the interfaces it must honour. Indexing lets it find related code on its own, but an explicit pointer to the key files sharpens the result. Keep each generation scoped and reviewable — a focused piece you can check beats a sprawling one you have to trust. Your rules do the rest, keeping style consistent without you restating it.
How does code generation in Cursor differ from a chatbot?
Cursor generates with your real project in context — referencing your files, indexing, and rules — so the code fits your codebase instead of standing apart, whereas a chatbot only sees what you paste.
How do I get Cursor to match my conventions?
Lean on project rules to carry your conventions and point it at the relevant files. Rules apply to every request, keeping generated code consistent without you restating your style.
How do I get better generated code from Cursor?
Reference the modules the code will touch and the interfaces it must honour, and keep each generation scoped and reviewable rather than sprawling.