Yes, vibe coding can reliably generate unit tests for your features, as long as you provide a clear contract for what “correct” means. The model is very good at turning a function signature, a textual specification, and a few examples into a set of tests that cover the obvious paths. To get useful tests, don’t just say “write tests for this file.” Instead, describe the scenarios you care about: typical input, edge cases, error conditions, and performance boundaries. Paste the function or class, and ask for deterministic, isolated tests that make no network calls and mock external services.
A practical pattern is to use vibe coding as a test generator before or alongside implementation. For instance, you can say, “Given this interface for a SearchService, write unit tests that verify we call Milvus only once per query, handle empty result sets, and reject vectors with the wrong dimensionality.” The model can then produce tests that target these behaviors explicitly. If your service calls a vector database like Milvus or Zilliz Cloud, ask it to create mocks or fakes rather than talking to the real cluster, and to assert that the correct collection name, search params, and filters are used.
However, you still need to review and harden these tests. Generated tests sometimes mirror the implementation too closely, re-encoding the same mistakes, or they skip interesting edge cases that weren’t described in your prompt. You can improve coverage by asking the model to “add property-based tests for these invariants” or “add tests for concurrent calls and timeouts.” Tools like mutation testing can further reveal gaps by showing which changes in the implementation aren’t caught by the tests. In short, vibe coding is a strong test-writing assistant, but you remain responsible for deciding what to test and for enforcing quality.