AI assistants work best on familiar patterns whose results are easy to check. Examples include boilerplate, simple data transformations, unit-test drafts, documentation, configuration, common framework code, and translations between similar languages. They can also summarize code or explain a stack trace quickly.
These tasks have many public examples, so the model has strong patterns to follow. A compiler, test, or short review can usually verify the result. Assistants are less reliable for new architecture, subtle concurrency, hidden business rules, and performance work that needs real measurements. Use them when the cost of checking the answer is low. Keep human judgment in charge when a mistake would be expensive or when correctness cannot be demonstrated with clear evidence.
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