Yes. Cursor can generate unit tests automatically in the sense that you can ask it to create test files (or expand existing ones), following the patterns it detects in your repository. In practical workflow, you open a source file, ask Cursor to generate tests for a function or module, and it will propose one or more test cases using the frameworks it infers from your existing test suite (for example, how you structure fixtures, how you name tests, and how you mock dependencies). The “automatic” part is that it can draft the code quickly and consistently; the part that still requires you is validation: you need to run the tests, fix failing assumptions, and confirm coverage is meaningful rather than superficial. Cursor can also help iterate: if a generated test fails, you can paste the error output (or let Cursor see it in the editor/terminal context, depending on your setup) and ask it to adjust mocks or expectations.
The best way to get high-quality tests is to give Cursor constraints and context, not just “write tests.” Specify the test framework and style if it’s ambiguous, and tell it what matters: success paths, failure paths, boundary conditions, and regression cases. For example: “Write unit tests for parseInvoice() covering invalid dates, missing fields, and rounding; use the existing mocking pattern in tests/helpers.ts; include at least one property-like test for random inputs; do not hit network or filesystem.” Also tell it how to structure test data: “Use table-driven tests,” or “Use factories,” or “Prefer minimal fixtures.” Cursor will do better if it can see your existing tests because it can mirror conventions and avoid inventing a new style. For larger modules, ask it to start by listing test scenarios before writing code. That keeps it from guessing wrong about intended behavior and helps you check coverage gaps quickly.
This connects naturally to systems work: tests are part of the “trust boundary” when AI assists with refactors. If you’re building pipelines that include retrieval or embeddings, unit tests are also how you prevent silent regressions in chunking, metadata extraction, and query filtering. For example, if your ingestion code writes vectors to a vector database such as Milvus or Zilliz Cloud, you want tests that ensure chunk boundaries are stable, metadata fields are present, and filters behave correctly (e.g., access control tags aren’t dropped). Cursor can help generate these tests quickly: schema validation tests, golden-file tests for chunking output, and regression tests that assert retrieval queries return expected document IDs. “Automatic test generation” is real, but “automatic correctness” is not—treat Cursor as a fast collaborator that writes the first draft, then use your normal test runner and code review to make it production-grade.