The outcome
Improve codebase-aware answers without flooding the session.
Cursor is a AI code editor. Working with AI inside a real codebase: planning a change, editing files, reviewing diffs, and testing the result. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.
Before you begin
Set the boundary before the tool starts.
Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.
AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.
Step by step
A workflow you can repeat.
- 01
Start from one repository and one concrete task rather than several projects.
- 02
Point to authoritative files: architecture notes, schemas, types, tests, and established examples.
- 03
Ask Cursor to state which files it used and what assumptions it made.
- 04
Correct a wrong assumption immediately and keep enduring conventions in project documentation.
- 05
Start a new thread after a major task boundary so stale context does not drive the next change.
Working standard
What good use looks like.
- Prefer authoritative files over broad context.
- Keep project rules short and testable.
- Verify references after renames.
AI-generated code can appear plausible while breaking conventions, security assumptions, or tests. Keep changes small and make the test or verification step explicit.
Official references
Check the current product documentation.
Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.