Zero shot not reliable

Zero-shot prompting is appealing because it is simple, but reliability can vary a lot depending on the task. When the problem is narrow and well understood, it may work fine. When the task is more nuanced, the model can drift quickly without examples or stronger guidance.

That does not mean zero-shot is useless. It means it has limits, especially when the output must follow a strict format or reflect domain-specific judgment.

Teams usually get better results by combining clear instructions with a few well-chosen examples or structured retrieval. Reliability improves when the model has more context about what success looks like.