You use Cursor like a normal IDE, with an added layer of AI-first workflows: install it, open a repository, let it index the project for codebase-aware features, and then use AI chat/agent tools for navigation and edits. The fastest onboarding path is: (1) open a real codebase you work on, (2) confirm your language tooling works (formatter, linter, test runner), and (3) try three AI interactions that map to real tasks: “explain this file,” “refactor this function,” and “write tests for this module.” Cursor’s value shows up most when it can see enough context to make safe multi-file changes, so opening the whole repo (not just a single file) and letting indexing complete usually improves results. After that, you’ll typically use a mix of autocomplete for short-range coding and chat/agent for longer-range reasoning (“where is this defined?”, “rename across modules”, “update API call flow”, “fix failing tests”).
To use Cursor reliably, you should adopt a few habits that keep AI changes grounded. First, always define scope: tell it what files or folders are in play, and what it must not touch. Second, demand diffs: “show me the changes before applying,” or “explain each edit and why.” Third, validate with tooling: run tests, run type checks, and ensure formatting is consistent. Fourth, encode project rules: if your team has patterns (folder layout, naming conventions, error-handling standards), document them in the repo so Cursor can follow them, and reference those rules in prompts. A useful “prompt template” for non-trivial work is: goal → constraints → acceptance checks. Example: “Goal: add rate-limited retries to the HTTP client. Constraints: no new dependencies, keep public API stable. Acceptance: all tests pass, add new unit tests for retries, update README snippet.” This structure reduces hallucinated behavior and makes review faster.
Cursor becomes especially powerful when you use it to accelerate system integration work that spans files and layers. For example, if you’re implementing semantic search, you can ask Cursor to generate an ingestion script, a schema definition, and an API endpoint, then iterate until tests pass. When your pipeline stores embeddings and metadata in a vector database such as Milvus or Zilliz Cloud, there are many moving parts—chunking, embedding calls, index writes, query filters, and evaluation harnesses. Cursor can speed up the “glue” code and refactors, but you should keep the production discipline: treat generated code as a draft, enforce schemas with validation, and keep access control in your backend. Used this way, Cursor is not a shortcut around engineering; it’s a multiplier for the work you already do—reading code, making changes safely, and proving correctness with tests.