📄 中文摘要
在与大型语言模型（LLM）互动时，用户常常会发现即使多次修改提问的措辞，得到的答案仍然不尽如人意。为了改善结果，用户可能会尝试重写提示、增加上下文，或使用诸如“简明扼要”或“逐步思考”等短语。然而，提示重复这一策略往往被忽视。通过在不同的上下文中多次重复相同的提示，用户可以引导模型更好地理解问题，从而提高回答的准确性和相关性。这种方法的有效性在于它可以帮助模型更清晰地捕捉用户的意图，进而生成更符合期望的结果。
提示重复：提升大型语言模型结果的被忽视技巧
出处: Prompt Repetition: The Overlooked Hack for Better LLM Results
发布: 2026年2月22日
📄 English Summary
Prompt Repetition: The Overlooked Hack for Better LLM Results
Interacting with large language models (LLMs) often leads users to rephrase their questions multiple times without achieving satisfactory answers. To enhance results, users may rewrite prompts, add context, or use phrases like 'be concise' or 'think step by step.' However, the strategy of prompt repetition is frequently overlooked. By repeating the same prompt in different contexts, users can better guide the model's understanding of the question, thus improving the accuracy and relevance of the responses. This method's effectiveness lies in its ability to help the model capture the user's intent more clearly, leading to results that better meet expectations.