AI-generated code can look polished while still being wrong. A model predicts likely text; it does not prove that the code matches your requirements. It may invent an API, miss an edge case, use an old library pattern, or create a security weakness. Generated tests can also pass while checking the wrong behavior.
You remain responsible for code that you commit. Read the full diff, confirm unfamiliar APIs, think through failure cases, and run suitable tests. Pay extra attention to authentication, data access, user input, and secrets. A helpful mindset is to treat the assistant as a fast contributor whose work always needs review. If you cannot explain a generated line or show how its behavior was verified, it is not ready to merge.
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