--- title: "少样本提示 - 交互与提示 - AI·外脑 | 思行致远" canonical: "https://siyuanailab.com/knowledge/ai-core/interaction/few-shot-prompting/" dateModified: "2026-06-29" datePublished: "2026-06-29" --- 打开 AI·外脑目录 AI·外脑 [返回专栏首页](https://siyuanailab.com/knowledge/) × 站内搜索 ⌕ 主要栏目 - [AI 核心概念](https://siyuanailab.com/knowledge/ai-core/) - [工作流](https://siyuanailab.com/knowledge/workflows/) - [工具配置与选型](https://siyuanailab.com/knowledge/toolkits/) - [信源与标准](https://siyuanailab.com/knowledge/sources/) - [场景案例 待开发](https://siyuanailab.com/knowledge/scenarios/) - [职业与能力](https://siyuanailab.com/knowledge/career/) 专题 - [从这里开始](https://siyuanailab.com/knowledge/start/) - [获取与安装](https://siyuanailab.com/knowledge/install/) - [建设日志](https://siyuanailab.com/knowledge/ops/) [AI 核心概念子目录](https://siyuanailab.com/knowledge/ai-core/) [基础概念 **9**](https://siyuanailab.com/knowledge/ai-core/foundations/) - [01-01 人工智能](https://siyuanailab.com/knowledge/ai-core/foundations/artificial-intelligence/) - [01-02 生成式 AI](https://siyuanailab.com/knowledge/ai-core/foundations/generative-ai/) - 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[05-01 系统提示词](https://siyuanailab.com/knowledge/ai-core/interaction/system-prompt/) - [05-03 少样本提示](https://siyuanailab.com/knowledge/ai-core/interaction/few-shot-prompting/) - [05-04 角色提示](https://siyuanailab.com/knowledge/ai-core/interaction/role-prompting/) - [05-05 提示词注入](https://siyuanailab.com/knowledge/ai-core/interaction/prompt-injection/) [安全与治理 **4**](https://siyuanailab.com/knowledge/ai-core/safety/) - [06-01 AI 安全](https://siyuanailab.com/knowledge/ai-core/safety/ai-safety/) - [06-03 智能体治理](https://siyuanailab.com/knowledge/ai-core/safety/agent-governance/) - [06-04 模型偏见](https://siyuanailab.com/knowledge/ai-core/safety/model-bias/) - [06-05 数据隐私](https://siyuanailab.com/knowledge/ai-core/safety/data-privacy/) 交互与提示 · 05-03 # 少样本提示（Few-shot Prompting） 杨思远 / 思行致远 · 2026/6/29 · 置信度：高 Few-shot PromptingPrompt示例 在提示中提供几个输入输出示例，让模型模仿格式、风格或判断标准。 # 少样本提示（Few-shot Prompting） > 在提示中提供几个输入输出示例，让模型模仿格式、风格或判断标准。 --- ## 一、是什么 - 在提示中提供几个输入输出示例，让模型模仿格式、风格或判断标准。 - 类比：像先给助理看合格样稿，再让他照着做。 - 关键澄清： | 它不是什么 | 它是什么 | | --- | --- | | 不是训练新模型 | 用上下文示例临时塑造模型行为 | ## 二、为什么重要 - 它适合快速稳定输出格式和风格。 - 不懂它会怎样：样例质量差会让模型照着犯错。 ## 三、日常怎么理解 - 用一句话讲给非技术朋友听：少样本提示就是给 AI 看样板。 - 常见场景：提供三条标准客服回复，再让模型处理新问题。 - **少样本学习（Few-shot Learning）视角**：少样本提示也体现了模型的少样本学习能力——即通过少量示例理解任务格式和规律，然后完成新样本。关键澄清：少样本提示不是重新训练模型，而是利用上下文示例引导模型行为。示例要少而准，过多会占用上下文并引入噪音。 ## 四、常见误解 | 误解 | 真相 | | --- | --- | | 样例越多越专业 | 示例要有代表性，并覆盖关键边界。 | | 示例越多越好 | 少样本提示不是训练新模型，示例要少而准，过多会占用上下文并引入噪音。 | ## 五、延伸阅读 - 本知识库相关：提示词工程 - [OpenAI Prompt engineering guide](https://platform.openai.com/docs/guides/prompt-engineering) - [Lilian Weng: Prompt Engineering](https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/) - [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) ## 适用场景与行动 **场景：**适合用来建立 AI 核心概念的共同语言。 **适合谁：**非技术创业者、AI 产品使用者、内容与业务负责人 **下一步：**先用一句话复述这个概念，再判断它和你的业务场景有什么关系。 这张卡片由杨思远基于真实工作流和实测经验整理。 如发现信息过时或有误，可通过 [/contact](https://siyuanailab.com/contact) 反馈。 [上一张：系统提示词](https://siyuanailab.com/knowledge/ai-core/interaction/system-prompt/) [下一张：角色提示](https://siyuanailab.com/knowledge/ai-core/interaction/role-prompting/) [返回当前分类](https://siyuanailab.com/knowledge/ai-core/interaction/) [返回 AI·外脑入口](https://siyuanailab.com/knowledge/)