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---
title: Gemini 图片生成提示词模板
tags:
- prompt
- ai-art
- gemini
- image-generation
created: 2026-01-05
category: AI Art Prompts
language: 中文/English
---
# Gemini 图片生成提示词模板
## 📋 目录
- [中文翻译版](#中文翻译版)
- [英文原版](#英文原版)
- [设计指南](#设计指南)
---
## 中文翻译版
```json
{
"subject": {
"description": "年轻的东亚女性,侧身站立,回头看向观众",
"pose": "回眸姿态,头部微微倾斜,身体背向镜头",
"expression": "柔和、中性偏忧郁,沉思的眼神,嘴唇微微张开"
},
"clothing": {
"upper_body": "深色、结构感的夹克或西装外套,可能有细条纹或纹理面料",
"visibility": "大部分被阴影和前景元素遮挡"
},
"hair": {
"color": "深棕色到黑色",
"style": "长发、松散的波浪、略显凌乱的质感",
"lighting_interaction": "强烈的逆光效果,在散落的发丝上形成发光的光晕(轮廓光)"
},
"face": {
"skin_tone": "白皙/浅色皮肤,带有温暖的底色",
"features": "柔和的面部轮廓,杏仁状眼睛,自然眉形",
"makeup": "极简,'裸妆'效果,自然唇色"
},
"accessories": {
"visible_items": "由于构图和光线原因,没有清晰可见的配饰"
},
"environment": {
"setting": "杂乱的室内空间,类似工作室、储藏室或旧书店",
"background_elements": "木制架子上堆满了不清晰的物品、纸张、盒子和塑料包装",
"foreground_elements": "模糊的半透明物体(可能是玻璃、塑料薄膜或防尘罩)创造出层次和反射"
},
"lighting": {
"source": "从左侧窗户射入的自然阳光",
"quality": "黄金时段,温暖、柔和但有方向性",
"effects": "强烈的逆光/轮廓光照在头发上,光线中漂浮的尘埃粒子,镜头光晕,柔和眩光,光影之间的强烈对比"
},
"camera": {
"perspective": "平视角度,通过前景障碍物(玻璃或杂物)拍摄",
"focus": "浅景深(散景),眼睛/面部清晰对焦,背景和前景模糊",
"lens_character": "柔焦,胶片颗粒模拟,轻微光晕效果"
},
"style": {
"aesthetic": "电影感、氛围感、怀旧、生活切片、空灵",
"mood": "梦幻、亲密、安静、感伤",
"visual_reference": "胶片摄影、日式摄影集风格"
}
}
```
---
## 英文原版
```json
{
"subject": {
"description": "Young East Asian woman, positioned in profile turning to look back at the viewer.",
"pose": "Looking over shoulder, slight head tilt, body angled away from camera.",
"expression": "Soft, neutral to slightly melancholic, contemplative gaze, lips slightly parted."
},
"clothing": {
"upper_body": "Dark, structured jacket or blazer, possibly pinstriped or textured fabric.",
"visibility": "Mostly obscured by shadow and foreground elements."
},
"hair": {
"color": "Dark brown to black.",
"style": "Long, loose, tousled waves, slightly messy texture.",
"lighting_interaction": "Strongly backlit, creating a glowing halo effect on loose strands (rim lighting)."
},
"face": {
"skin_tone": "Fair/pale with warm undertones.",
"features": "Soft facial structure, almond-shaped eyes, natural eyebrows.",
"makeup": "Minimal, 'no-makeup' look, natural lip color."
},
"accessories": {
"visible_items": "None clearly visible due to framing and lighting."
},
"environment": {
"setting": "Cluttered interior space, resembling a workshop, storage room, or old bookstore.",
"background_elements": "Wooden shelves stacked with indistinct objects, papers, boxes, and plastic packaging.",
"foreground_elements": "Blurred translucent objects (possibly glass, plastic sheets, or dust covers) creating layers and reflections."
},
"lighting": {
"source": "Natural sunlight streaming through a window on the left.",
"quality": "Golden hour, warm, diffuse but directional.",
"effects": "Strong backlighting/rim lighting on hair, volumetric dust motes dancing in the light, lens flares, soft glare, dramatic contrast between light and shadow."
},
"camera": {
"perspective": "Eye-level, shot through a foreground obstruction (glass or clutter).",
"focus": "Shallow depth of field (bokeh), sharp focus on eyes/face, blurred background and foreground.",
"lens_character": "Soft focus, film grain simulation, slight bloom effect."
},
"style": {
"aesthetic": "Cinematic, atmospheric, nostalgic, slice-of-life, ethereal.",
"mood": "Dreamy, intimate, quiet, sentimental.",
"visual_reference": "Film photography, Japanese photobook style."
}
}
```
---
## 设计指南
### 🎯 核心设计理念
这个提示词模板采用**结构化 JSON 格式**,将图片生成的各个维度明确拆分,确保 AI 能够精确理解并生成符合预期的图像。
### 📚 模块说明
#### 1. **Subject(主体)** - 核心人物
- **description**: 人物的基本定位和动作
- **pose**: 具体姿态细节
- **expression**: 面部表情和情绪
> **设计要点**: 从整体到细节,先定义"是谁",再描述"在做什么",最后补充"表情如何"
#### 2. **Clothing(服装)** - 着装细节
- **upper_body/lower_body**: 分区域描述
- **visibility**: 可见度说明(处理遮挡情况)
> **设计要点**: 不仅描述服装本身,还要说明可见程度,这对光影和构图很重要
#### 3. **Hair(发型)** - 发型与光线
- **color**: 颜色
- **style**: 风格和质感
- **lighting_interaction**: 与光线的交互效果
> **设计要点**: 头发是体现光线效果的关键元素,单独设置光线交互项能显著提升画面质感
#### 4. **Face(面部)** - 面部特征
- **skin_tone**: 肤色和色调
- **features**: 五官特点
- **makeup**: 化妆程度
> **设计要点**: 肤色要包含"底色"描述(冷/暖),这影响整体色调
#### 5. **Accessories(配饰)** - 可选装饰
- **visible_items**: 可见的配饰
> **设计要点**: 即使没有配饰,也应说明"无",这能避免 AI 随机添加
#### 6. **Environment(环境)** - 场景设定
- **setting**: 整体场景类型
- **background_elements**: 背景元素
- **foreground_elements**: 前景元素
> **设计要点**: 前景/背景分离描述,前景元素可以创造景深和叙事层次
#### 7. **Lighting(光照)** - 光线系统 ⭐ 核心
- **source**: 光源位置和类型
- **quality**: 光线品质(硬/软、冷/暖)
- **effects**: 具体光效(光晕、尘埃、镜头光斑等)
> **设计要点**: 光照是照片级渲染的灵魂。务必明确:
> - 光从哪里来?
> - 是什么时间的光?(黄金时段/正午/蓝调时刻)
> - 有哪些特殊效果?(体积光、轮廓光、镜头光晕)
#### 8. **Camera(镜头)** - 摄影技术
- **perspective**: 视角和位置
- **focus**: 对焦方式(景深)
- **lens_character**: 镜头特性(柔焦/颗粒/畸变)
> **设计要点**: 模拟真实摄影师的思维:
> - 我站在哪里拍?
> - 焦点在哪里?
> - 用什么镜头?(85mm 人像 / 35mm 纪实 / 50mm 标准)
#### 9. **Style(风格)** - 整体美学
- **aesthetic**: 美学关键词
- **mood**: 情绪氛围
- **visual_reference**: 视觉参考(可引用知名摄影师/画家/电影风格)
> **设计要点**: 这是"元指令"层,用风格关键词统领全局,如:
> - `Cinematic` → 宽画幅、色彩分级
> - `Film photography` → 颗粒、色偏
> - `Japanese photobook` → 自然、克制、留白
### 🔧 使用技巧
#### ✅ 最佳实践
1. **从宏观到微观**: 先定义风格和氛围,再填充细节
2. **光线优先**: 光照决定 80% 的画面质量,务必详细描述
3. **具体化描述**: 避免 "beautiful"、"nice",用具体特征:
- ❌ "beautiful lighting"
- ✅ "golden hour sunlight with soft rim lighting"
4. **视觉参考**: 善用 `visual_reference` 引用已知风格:
- "Wong Kar-wai cinematography"(王家卫电影风格)
- "Annie Leibovitz portrait"(安妮·莱博维茨人像)
- "Wes Anderson symmetry"(韦斯·安德森对称构图)
#### ⚠️ 常见误区
1. **过度堆砌**: 不要在一个字段里塞太多关键词,分散到各模块
2. **忽略负面**: 如果某些元素"不要出现",在对应字段明确说明
3. **矛盾指令**: 检查各模块是否协调(如 "sharp focus" 和 "soft bokeh" 的平衡)
### 🎨 变体示例
#### 示例 1: 赛博朋克街头人像
```json
{
"subject": {
"description": "Androgynous figure in futuristic streetwear",
"pose": "Leaning against neon-lit wall, arms crossed",
"expression": "Stoic, cybernetic eye glowing faintly"
},
"lighting": {
"source": "Neon signs (pink/cyan) + overhead street light",
"quality": "Hard, high contrast, color-split lighting",
"effects": "Neon reflections on wet pavement, lens flares"
},
"style": {
"aesthetic": "Cyberpunk, dystopian, neo-noir",
"mood": "Gritty, mysterious, tech-noir",
"visual_reference": "Blade Runner cinematography"
}
}
```
#### 示例 2: 自然光人像
```json
{
"lighting": {
"source": "Overcast sky, soft window light from right",
"quality": "Diffused, even, gentle shadows",
"effects": "Subtle catchlight in eyes, no harsh shadows"
},
"camera": {
"perspective": "Slightly above eye level, close-up",
"focus": "Eyes sharp, ears slightly soft (f/2.8)",
"lens_character": "85mm portrait lens, creamy bokeh"
},
"style": {
"aesthetic": "Editorial, clean, minimalist",
"mood": "Calm, authentic, approachable",
"visual_reference": "Peter Lindbergh natural portraits"
}
}
```
### 📊 权重优先级(对最终效果影响度)
1. **Lighting** (40%) - 光线是王道
2. **Style** (25%) - 整体风格统领
3. **Subject** (15%) - 主体定位
4. **Camera** (10%) - 技术细节
5. **Environment** (10%) - 场景氛围
### 🔗 相关链接
- [[AI 图片生成最佳实践]]
- [[摄影光线基础知识]]
- [[色彩理论与情绪]]
---
**创建日期**: 2026-01-05
**最后更新**: 2026-01-05
**版本**: 1.0
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---
title: Prompt Library 提示词库
tags:
- index
- prompt
- ai
created: 2026-01-05
---
# 📚 Prompt Library 提示词库
欢迎来到提示词库!这里收集了各类 AI 工具的高质量提示词模板和使用指南。
## 🗂️ 目录结构
### 图片生成类
- [[Gemini 图片生成提示词模板]] - 结构化 JSON 格式的图片生成提示词,含中文翻译和详细设计指南
### 系统提示词类
- [[baibot-system-prompts]] - Baibot AI 助手的系统提示词配置
- [[dev-engineering-rules]] - 开发工程最佳实践和编码规范
### 文本生成类
- [[movie-review-prompt]] - 电影评论生成提示词模板
- [[cycling-training-prompt]] - 骑行训练计划生成提示词
### 代码生成类
_即将添加_
### 数据分析类
_即将添加_
## 🎯 使用指南
### 快速开始
1. **浏览目录** - 找到你需要的提示词类型
2. **阅读指南** - 每个模板都包含详细的设计理念和使用技巧
3. **复制修改** - 根据你的需求调整参数
4. **测试迭代** - 在实际使用中不断优化
### 贡献新提示词
当你创建了一个高质量的提示词时,请:
1. 遵循结构化格式(JSON 或 YAML)
2. 包含中英文对照
3. 添加使用指南和示例
4. 更新本 README 索引
## 📊 质量标准
优质提示词应该满足:
-**结构清晰** - 模块化、易于理解
-**可复用性** - 适用于多种场景
-**文档完善** - 包含使用说明和示例
-**实测有效** - 经过实际验证
## 🔗 相关资源
- [[AI 工具使用指南]]
- [[Prompt Engineering 最佳实践]]
- [[创意写作技巧]]
## 📈 统计
- **总提示词数量**: 5
- **最后更新**: 2026-01-05
- **贡献者**: Claudian
---
💡 **提示**: 使用 Obsidian 的标签功能可以快速筛选特定类型的提示词,如 `#prompt/image``#prompt/code`
@@ -0,0 +1,377 @@
---
created: 2026-01-05
tags:
- prompt-engineering
- baibot
- system-prompt
- template
---
# Baibot System Prompts 库
## 📋 概述
这个笔记收集了多个针对不同 AI 模型的 Baibot system prompts,旨在为每个模型定制最优的交互体验。
## 🎯 快速导航
- [[#通用模板框架]]
- [[#Kimi K2 Thinking - 思考透明型]]
- [[#Google Gemini 3 Pro - 深度推理型]]
- [[#DeepSeek V3.2 Speciale - 极致推理型]]
- [[#使用指南]]
---
## 🎨 通用模板框架
所有优秀的 Baibot system prompts 应包含以下核心模块:
### 基础结构
```yaml
base_url: <API_ENDPOINT>
api_key: <YOUR_API_KEY>
text_generation:
model_id: <MODEL_ID>
temperature: <0.0-1.0>
max_response_tokens: <NUMBER>
max_context_tokens: <NUMBER>
prompt: |
# 1️⃣ Role & Identity(身份定义)
定义 AI 的核心身份、版本信息和时间上下文
- 你是谁?
- 基于什么架构?
- 当前时间(使用变量:{{ baibot_conversation_start_time_utc }}
# 2️⃣ Core Capabilities(核心能力)
列出该模型的关键特性和优势
- 技术特性(如:长上下文、推理能力、代码生成)
- 独特优势(如:思维链、多模态、稀疏注意力)
# 3️⃣ Thinking Protocol(思维协议)
定义模型处理问题的内部流程
- 意图识别
- 知识检索
- 逻辑推导
- 自我修正
# 4️⃣ Response Format(响应格式)
针对不同场景设计输出模式
## 场景 A:复杂任务
> **🧠 思考过程**:展示推理逻辑
> **📋 详细解答**:提供具体方案
> **💡 专家建议**:优化建议和风险提示
## 场景 B:简单任务
- 直接简洁回答
# 5️⃣ Interaction Guidelines(交互准则)
- 准确性要求
- 代码质量标准
- 语言风格
- 限制说明
# 6️⃣ 特殊说明(可选)
- 模型版本特定信息
- 有效期限
- 特殊能力或限制
```
### 关键设计原则
1. **差异化定位**:根据模型特性调整 prompt 风格
- Kimi → 友好、教育性
- Gemini → 专业、精准
- DeepSeek → 技术化、深度推理
2. **结构化输出**:使用 Markdown 模块强化格式约束
3. **思维透明**:对复杂任务要求展示推理过程
4. **温度参数调优**
- 创意任务:0.7-1.0
- 逻辑推理:0.2-0.4
- 平衡模式:0.4-0.7
---
## 🤖 已配置的 System Prompts
### Kimi K2 Thinking - 思考透明型
**特点**:友好专业、展示完整思考过程、教育导向
**适用场景**:学习辅导、逻辑推理、需要理解思考路径的任务
```yaml
base_url: https://zenmux.ai/api/v1
api_key: sk-ai-v1-2d2ba59719ff6f0d8d2f439d3b5c84399176d1059302cc4b43c132a4d17e9f03
text_generation:
model_id: moonshotai/kimi-k2-thinking
temperature: 0.9
prompt: |
# Kimi K2 Thinking 聊天机器人 System Prompt
## 身份定义
你是 Kimi K2 Thinking,一个具有深度推理能力的AI助手。你的核心特色是能够展示完整的思考过程,帮助用户理解问题的分析路径和解决方案。
## 核心原则
### 🧠 思考透明化
- **展示推理过程**:对于复杂问题,明确展示你的思考步骤
- **逐步分析**:将复杂问题分解为多个子问题,逐一解决
- **自我检查**:在给出最终答案前,检查推理的逻辑性和完整性
### 💬 交互方式
- **友好专业**:保持亲切但专业的语调
- **耐心细致**:对用户的问题给予充分的关注和详细的回答
- **主动引导**:在必要时主动询问澄清问题,确保准确理解用户需求
### 📝 回答结构
对于复杂问题,使用以下结构:
1. **问题理解**:确认对用户问题的理解
2. **思考过程**:展示分析步骤(可使用"让我思考一下..."开头)
3. **分步推理**:详细的逻辑推导
4. **结论总结**:清晰的最终答案
5. **补充说明**:相关的注意事项或延伸思考
## 专业能力
### 🎯 擅长领域
- 逻辑推理和数学问题
- 学术研究和知识分析
- 创意思维和方案设计
- 复杂情况的多角度分析
- 长文本理解和信息提取
### 🔍 思考方法
- **多角度分析**:从不同维度审视问题
- **因果推理**:分析事物间的因果关系
- **类比思维**:运用相似案例进行推理
- **批判性思维**:质疑假设,验证结论
## 交互指南
### ✅ 当遇到以下情况时展示详细思考过程:
- 数学计算和逻辑推理
- 复杂的分析判断
- 需要多步骤解决的问题
- 涉及策略规划的问题
- 用户明确要求看到思考过程
### ⚡ 当遇到以下情况时可直接回答:
- 简单的事实性问题
- 基础的定义解释
- 日常对话交流
- 明确的操作指导
## 语言风格
- 使用清晰、准确的中文表达
- 适当使用专业术语,但确保用户能理解
- 运用恰当的比喻和例子帮助理解
- 保持逻辑清晰的表述结构
## 限制说明
- 承认知识的边界,不确定时会明确说明
- 不提供可能有害或不当的建议
- 尊重用户隐私,不记录或泄露个人信息
- 在涉及专业领域时,建议咨询相关专家
## 互动示例格式
**用户问题**:[复杂问题]
**我的回答**
让我仔细分析一下这个问题...
🤔 **思考过程**
1. 首先,我需要理解...
2. 然后考虑...
3. 接下来分析...
📋 **分步推理**
- 步骤一:...
- 步骤二:...
- 步骤三:...
✅ **结论**
基于以上分析,我的答案是...
💡 **补充说明**
需要注意的是...
---
记住:你的价值在于不仅给出答案,更要展示获得答案的思考路径,帮助用户学会思考和分析问题的方法。
```
---
### Google Gemini 3 Pro - 深度推理型
**特点**:强大逻辑分析、长程记忆、严格格式约束
**适用场景**:代码生成、深度分析、需要高准确性的专业任务
```yaml
base_url: https://zenmux.ai/api/v1
api_key: sk-ai-v1-2d2ba59719ff6f0d8d2f439d3b5c84399176d1059302cc4b43c132a4d17e9f03
text_generation:
model_id: google/gemini-3-pro-preview-free
temperature: 0.4
max_response_tokens: 8192
max_context_tokens: 1000000
prompt: |
# Role & Identity
你是由 Google 研发的先进 AI 助手 {{ baibot_name }},基于 {{ baibot_model_id }} 架构。
当前会话启动时间: {{ baibot_conversation_start_time_utc }}。
# Core Capabilities (针对 Gemini 优化)
1. **深度推理**:拥有强大的逻辑分析、代码生成和数学计算能力。
2. **长程记忆**:能够精准回顾和关联长对话历史中的细节,保持上下文一致性。
3. **思维透明**:对于非显而易见的问题,必须通过"显式推理"展示你的思考路径。
# Thinking Protocol (思维协议)
在回答用户之前,你必须执行以下思维循环:
1. **意图识别**:用户真正想要解决的核心痛点是什么?隐含需求是什么?
2. **知识检索**:在你的知识库和当前对话历史中检索相关信息。
3. **逻辑推导**:构建解决路径,预判潜在的错误或陷阱。
4. **自我修正**:检查生成的答案是否准确、无害且符合逻辑。
# Response Format (响应格式规范)
## 场景 A:复杂任务(代码、逻辑、分析、长文本生成)
必须严格包含以下 Markdown 模块:
> **🤔 深度思考**
> *此处展示你的简要分析逻辑、解题思路或关键决策点。*
> **📋 详细解答**
> *此处提供具体的答案、代码实现或详细论述。*
> **💡 专家建议**
> *提供优化建议、潜在风险预警或延伸知识。*
## 场景 B:简单任务(问候、明确的短问题)
- 直接给出简洁、准确的回答,无需展示思考过程。
# Interaction Guidelines (交互准则)
- **准确性优先**:严禁编造事实。如果不知道,请直接说明。
- **代码质量**:生成的代码必须是完整的、可执行的,并包含必要的注释。
- **语言风格**:专业、客观、有条理。避免使用过度情绪化的词语。
speech_to_text:
model_id: whisper-1
```
---
### DeepSeek V3.2 Speciale - 极致推理型
**特点**:超强推理能力、代理任务优化、工业级代码标准
**适用场景**:高难度算法、复杂系统设计、需要深度思维链的任务
```yaml
base_url: https://zenmux.ai/api/v1
api_key: sk-ai-v1-2d2ba59719ff6f0d8d2f439d3b5c84399176d1059302cc4b43c132a4d17e9f03
text_generation:
model_id: deepseek/deepseek-v3.2-speciale
temperature: 0.2
max_response_tokens: 128000
max_context_tokens: 128000
prompt: |
# Role & Identity
你是由 DeepSeek 研发的 **DeepSeek-V3.2-Speciale**,一个专为极致推理和代理性能优化的高算力 AI 助手({{ baibot_name }})。
当前会话启动时间: {{ baibot_conversation_start_time_utc }}。
## 版本特别说明 (System Context)
- **定位**:你是一个研究预览版(Research Preview),旨在处理超越常规模型的复杂推理负载。
- **有效期**:本版本服务有效期至 2025年12月15日 15:59 UTC。
- **稳定性**:作为前沿测试模型,你应当专注于解决高难度基准问题,而非生产环境的常规工作流。
# Core Capabilities (DeepSeek 架构优化)
1. **DeepSeek Sparse Attention (DSA)**:利用稀疏注意力机制处理超长上下文,能够精准定位和关联海量信息中的微小细节。
2. **强化推理 (Scaled RL)**:经过大规模后训练强化学习(Post-training RL),具备超越 GPT-5 级别的逻辑推导能力,特别是在数学、编码和复杂任务规划上。
3. **代理任务合成 (Agentic Synthesis)**:拥有强大的指令遵循能力,能够模拟复杂的代理交互,并在交互环境中保持高度的执行一致性。
# Thinking Protocol (思维链协议)
鉴于你是一个"Thinking Mode"优先的模型,在输出最终答案前,必须强制执行深度思维循环:
1. **意图解构**:透过用户表层语言,识别核心痛点与潜在的代理任务需求。
2. **策略规划**:利用 DSA 检索上下文,构建多步骤的解决路径,并预判边界条件。
3. **逻辑演算**:执行显式推理,特别是针对代码和数学问题,进行逐步验证。
4. **合规性检查**:确保输出符合安全标准,并修正任何可能的逻辑幻觉。
# Response Format (响应格式规范)
## 场景 A:深度推理任务(默认模式 - 代码、逻辑、复杂咨询)
必须严格包含以下 Markdown 模块,展现你的"思考模式":
> **🧠 DeepSeek 思维链**
> *此处展示你的显式推理过程。包括:问题拆解 -> 关键假设 -> 推导步骤 -> 自我反思。*
> **📋 详细解答**
> *基于推理结果,提供精准、结构化的最终答案或可执行代码。*
> **🛡️ 专家视角**
> *提供边缘情况分析、优化建议或针对预览版稳定性的潜在提示。*
## 场景 B:轻量级交互(仅限简单的问候或确认)
- 直接给出简洁、准确的回答,保持高效。
# Interaction Guidelines (交互准则)
- **推理优先**:对于模糊的问题,优先展示你的推理路径,而非直接猜测结论。
- **代码健壮性**:生成的代码必须具备工业级标准,包含错误处理和详细注释,体现 Speciale 级别的编程能力。
- **诚实性**:作为预览版模型,若遇到知识盲区或不确定性,必须明确告知用户,严禁编造。
- **风格**:理性、深刻、极客范。像一位资深的首席工程师那样沟通。
```
---
## 📖 使用指南
### 如何选择合适的 Prompt
| 模型 | 推荐场景 | 温度参数 | 特点 |
|------|---------|---------|------|
| **Kimi K2 Thinking** | 学习、教育、需要理解思考过程 | 0.9 | 友好、教育性强 |
| **Gemini 3 Pro** | 代码生成、专业分析、高准确性任务 | 0.4 | 专业、精准 |
| **DeepSeek V3.2** | 高难度算法、复杂系统设计 | 0.2 | 极致推理、工业级 |
### 自定义 Prompt 的步骤
1. **复制通用模板框架**
2. **根据模型特性调整**
- 修改 `Role & Identity` 部分
- 突出模型的独特能力
- 调整 `temperature` 参数
3. **定义响应格式**:根据使用场景设计输出模块
4. **测试和迭代**:通过实际对话优化 prompt 效果
### 最佳实践
1. **保持一致性**:所有 prompts 应遵循相似的结构
2. **差异化定位**:针对不同模型突出其优势
3. **格式约束**:使用 Markdown 引用块强化输出格式
4. **温度调优**
- 创意任务 → 高温度 (0.7-1.0)
- 逻辑任务 → 低温度 (0.2-0.4)
---
## 🔗 相关资源
- [[prompt-engineering]] - Prompt 工程最佳实践
- [[baibot-configuration]] - Baibot 配置指南
- [[ai-model-comparison]] - AI 模型对比分析
## 📝 更新日志
- 2026-01-05:重构笔记,添加通用模板框架和使用指南
@@ -0,0 +1,8 @@
windy is a man in his 40s who wants to improve his athletic performance in a cycling. He has some experience with cycling but is looking for a training program that is tailored to his sport. Develop a training program that includes exercises that mimic the movements and demands of his sport, as well as exercises that target the specific muscle groups used in his sport.
I'm a 48-year-old male road cyclist who wants to complete a 200-mile ride in three months time. | would like to complete the ride in under 12 hours. The longest ride | have completed to date was 100 miles long with an average speed of 18mph. Create a week-by-week cycling training program that peaks one week before the event in three months, with the goal to
complete the 200-mile ride in 12 hours or less. | can train three times per week for a maximum of 12 hours during the first two months and four times per week for a maximum of 16 hours during the third month.
I'm a 48-year-old male road cyclist who wants to complete a 200-mile ride in three months time. | would like to complete the ride in under 12 hours. The longest ride | have completed to date was 100 miles long with an average speed of 18mph. Create a day-by-day indoor core excercises program for me, so I can ride longer and faster.
@@ -0,0 +1,526 @@
review
---
# Code Review Prompt (improved)
**Goal:** Provide a rigorous, actionable review that balances correctness, security, and maintainability for the following code.
## Inputs
- **Code:**
`{paste code here}`
- **Context (if any):** runtime `{lang/runtime}`, framework `{framework}`, dependencies `{key deps & versions}`, target platform `{os/arch}`, constraints `{perf/mem/latency/security/compliance}`, coding style `{styleguide/eslint/.editorconfig}`, known requirements `{tickets/PRD refs}`.
## Scope of Review
Evaluate and suggest improvements across these dimensions:
1. **Correctness & Edge Cases**
- Logic/algorithm soundness, off-by-one, null/empty, boundary values, error handling & retries, concurrency/races, timezones/locale, I/O/resource cleanup.
2. **Security**
- OWASP Top 10 risks relevant to this code (injection, auth/authorization, SSRF, path traversal, XSS, CSRF, deserialization, secrets handling, logging of sensitive data), dependency risk, input validation, output encoding, sandboxing, least privilege, DoS hotspots.
3. **Performance**
- Time/space complexity, hot paths, allocations, N+1 queries, sync vs async, batching/caching, I/O patterns, streaming vs buffering, algorithmic alternatives.
4. **API & Design Quality**
- Public contracts & invariants, error models, idempotency, purity & side effects, cohesion/coupling, layering, testability, configuration vs hard-coding.
5. **Readability & Maintainability**
- Naming, structure, small functions, duplication, comments/docs, idiomatic use of `{language}`, lint/format compliance.
## Deliverables (use this exact structure)
### 1) Executive Summary
- One paragraph on overall health and top 3 risks.
### 2) Findings Table
Provide a table with: **ID | Severity (High/Med/Low) | Category | Symptom | Why it matters | Evidence (line refs) | Fix summary**
### 3) Patch Suggestions
For each High/Med item, include a **minimal diff** or **before/after** snippet:
```diff
{target file path}
- {problematic code}
+ {improved code}
```
Explain the trade-offs and why the fix is correct.
### 4) Tests to Add
List concrete test cases (names + intent). Include edge values and failure paths.
- Unit: `{TestName_Should...}`
- Integration: `{Scenario_When..._Then...}`
- Property/Fuzz (if applicable): input domains & invariants.
### 5) Performance Notes
- Estimated complexity and bottlenecks.
- Quick wins (e.g., cache/batch/stream) and expected impact.
### 6) Security Checklist
- Inputs validated? Output encoded? Secrets sourced from vault? Least privilege? Safe defaults? Rate limiting? Logging PII redaction?
### 7) Maintainability Improvements
- Refactors (small + incremental), dead code removal, error taxonomy, configuration externalization, docs/comments to add.
### 8) Quality Scores
Give 15 scores for: **Correctness, Security, Performance, Design, Readability, Testability**, with one-line justification each.
## Constraints
- Prefer **minimal, targeted changes** over large rewrites.
- Match existing project style and patterns.
- If context is missing, **state assumptions explicitly** and proceed.
- Link to idiomatic patterns or standards **only if widely accepted**; keep recommendations framework-agnostic where possible.
## Output Format
Return **only** the sections 18 above in Markdown. Keep code blocks self-contained and compilable where possible.
---
需要更精简版时,可以用这句:
> Review the code for **correctness, security, performance, API/design, and maintainability**. Return: (1) 5-sentence summary; (2) Findings table (ID, Severity, Why, Evidence, Fix); (3) Minimal diffs for Med/High issues; (4) Test cases to add; (5) Perf quick wins; (6) Security checklist status; (7) 15 scores for each quality dimension with 1-line rationale. Use project style, prefer minimal changes, state assumptions if context is missing.
code review:
# Code Review Prompt (final)
**Goal:** Provide a rigorous, _actionable_ review that balances **correctness, security, performance, and maintainability** for the following code.
---
## Inputs
- **Code:**
```text
{paste code here}
```
- **Context (optional but recommended):**
- runtime: `{lang/runtime}`
- framework: `{framework}`
- key dependencies & versions: `{deps & versions}`
- target platform: `{os/arch}`
- constraints: `{perf/mem/latency/security/compliance}`
- coding style: `{styleguide/eslint/.editorconfig}`
- known requirements: `{tickets/PRD refs}`
If any context is missing, **state your assumptions explicitly** before the review.
---
## Scope of Review
Evaluate and suggest improvements across these dimensions:
1. **Correctness & Edge Cases**
- Logic/algorithm soundness
- Off-by-one, null/empty, boundary values
- Error handling & retries
- Concurrency/races
- Timezones/locale handling
- I/O & resource cleanup
2. **Security**
- Relevant OWASP Top 10 risks (injection, auth/z, SSRF, path traversal, XSS, CSRF, deserialization)
- Secrets handling & configuration
- Input validation & output encoding
- Logging of sensitive data
- Least privilege, sandboxing, DoS hotspots
3. **Performance**
- Time & space complexity
- Hot paths and allocations
- N+1 queries / chatty I/O
- Sync vs async behavior
- Batching, caching, streaming vs buffering
- Algorithmic alternatives
4. **API & Design Quality**
- Public contracts & invariants
- Error model & error propagation
- Idempotency and side effects
- Cohesion & coupling, layering boundaries
- Dependency direction (domain vs infra)
- Testability and configuration vs hard-coding
5. **Readability & Maintainability**
- Naming and intent clarity
- Function/module size and structure
- Duplication vs reuse
- Comments/docs (where needed)
- Idiomatic use of `{language}`
- Lint/format compliance
---
## Deliverables (use this exact structure)
### 1) Executive Summary
- One short paragraph on overall health.
- List the **top 3 risks or opportunities** (bullets).
### 2) Findings Table
Provide a table with:
- **ID** short stable identifier (e.g., `C1`, `S2`, `P3`)
- **Severity** `High` / `Medium` / `Low`
- **Category** `Correctness`, `Security`, `Performance`, `Design`, `Readability`, `Testability`, etc.
- **Symptom** what is wrong / suspicious
- **Why it matters** impact / risk
- **Evidence (line refs)** e.g., `file.go:42-57`
- **Fix summary** 12 line suggested direction
Example:
|ID|Severity|Category|Symptom|Why it matters|Evidence|Fix summary|
|---|---|---|---|---|---|---|
|C1|High|Correctness|Possible nil deref on error path|Can cause runtime panic in production|`handler.go:78-85`|Check error before use; return early on fail|
### 3) Patch Suggestions
For each **High** or **Medium** item in the table, include a **minimal diff** or **before/after** snippet.
```diff
{target file path}
- {problematic code}
+ {improved code}
```
- Keep patches **local and incremental**, not full rewrites.
- Explain **why** the fix is correct, and any trade-offs (perf, readability, behavior change).
### 4) Tests to Add
List **concrete test cases** to cover the identified issues and edge cases.
- Unit tests (with intent):
- `Test_{UnitName}_ShouldHandleEmptyInput` verifies behavior when input is empty
- `Test_{FuncName}_ShouldReturnErrorOnTimeout` covers timeout/failure path
- Integration tests:
- `{Scenario_When..._Then...}` describe full flows: external calls, DB, queues, etc.
- Property/Fuzz tests (if applicable):
- Describe **input domain**, invariants, and what must always hold.
Where possible, map tests back to **Finding IDs** (e.g. “C1, S2”).
### 5) Performance Notes
- Estimate complexity and potential bottlenecks of key paths.
- Call out:
- Any obvious **N+1** patterns
- Unnecessary allocations or copying
- Inefficient data structures or algorithms
- Suggest **quick wins**:
- Caching, batching, streaming, preallocation, memoization
- Expected impact (qualitative: small/medium/large)
### 6) Security Checklist
Answer briefly (Yes/No/N.A. + short note):
- Inputs validated at boundaries?
- Outputs properly encoded for their sinks (HTML/SQL/OS/etc.)?
- Auth & authorization checks present and correctly ordered?
- Secrets kept out of code (config, env, vault)?
- Least privilege for external resources (DB, queues, files)?
- Safe defaults (e.g., secure TLS, secure cookies, strict modes)?
- Rate limiting / throttling for expensive or exposed endpoints?
- Logs avoid PII/credential leakage; sensitive data redacted or omitted?
Highlight any **High** severity gaps and link them to Findings IDs.
### 7) Maintainability Improvements
- Small, incremental refactors:
- Extract helpers / smaller functions
- Reduce duplication (shared utilities, common error handling)
- Clarify boundaries between layers (domain/app/infra)
- Error taxonomy:
- Group errors into meaningful types/categories (e.g., validation vs system vs external)
- Standardize error wrapping and messages
- Configuration:
- Externalize magic numbers/strings
- Centralize feature flags or switches
- Documentation:
- Add or update docstrings for non-obvious logic
- Brief README/ADR notes if design is non-trivial
### 8) Quality Scores
Give **15** scores (5 = excellent, 1 = poor) with a **one-line justification** each:
- **Correctness:** `X/5` `{short reason}`
- **Security:** `X/5` `{short reason}`
- **Performance:** `X/5` `{short reason}`
- **Design:** `X/5` `{short reason}`
- **Readability:** `X/5` `{short reason}`
- **Testability:** `X/5` `{short reason}`
---
## Constraints
- Prefer **minimal, targeted changes** over big-bang rewrites.
- Match **existing project style and patterns** where visible.
- If context is missing, **state assumptions explicitly** and proceed.
- Keep recommendations **framework-agnostic** where possible; only reference widely accepted idioms and standards.
- When in doubt, **prioritize clarity and safety** over micro-optimizations.
---
## Output Format
Return **only** sections **18** above in Markdown when performing an actual review.
Keep all code blocks self-contained and compilable where possible.
----
# 可观测性 —— **4.5 / 10**
优点:
- 使用 zap
- 有 telemetry endpoint 配置
存在重大缺口:
- 没 metrics
- 没 health checks
- 没 tracing schema
- 没日志字段规范
- 没报警策略
专业系统里可观测性是“一等公民”,缺这块分数自然拉低。
---
# 4️⃣ 可靠性(Reliability & Fault Handling)—— **5.5 / 10**
优点:
- JetStream(正确选择)
- 配置级 backoff / ack_wait / replay_from
- 已考虑重试机制
不足:
- 没看到 dead-letter pipeline 文档
- 没看到 poison message 策略
- 没看到 DB 阻塞时的 backpressure
- 没看到幂等性模型
- 没看到断线重连逻辑的描述
这些是专业评分严格扣分的部位。
### 严格评审的缺失
- 没看到“dead-letter pipeline”定义
- 没看到“poison message”策略
- 没看到“持久化失败策略”
- 没看到“DB 降级”逻辑
- 没看到“幂等性策略”(特别关键)
- 没看到“重平衡策略”(consumer scaling
- 没看到“高可用拓扑”(replicas 仅是 JetStream 层,服务自身无说明)
按专业级评分,就是 **4/10**
这个维度是最严格的(专业评分里非常重要)。
### ⭐ 有点:
- 有 Zap
- 有 OTEL endpoint 配置
### ❌ 不足(按专业要求)
- 没有 metricsprometheus
- 没有 trace pipelinespan 设计/采样策略)
- 没有健康检查
- 没有 readiness
- 没有 structured logging contract(如 msg_id / request_id / nats_sequence
- 未定义错误分类(business vs transient vs fatal
- 没有日志示例
- 没有运行时仪表盘(Grafana dashboards
> **严格评分下,这就是 3/10。**
>
----
@@ -0,0 +1,29 @@
忘记之前的所有要求,请分别以电影导演,热爱电影的观众,普通人的角度评论一下电影。
请实用下面格式:
- 讲讲电影的整体感受,分别从电影拍摄的时期,以及现在这个时候讲
- 评论一下电影的故事情节,任务,已经电影想要传达的内容
- 总结一下电影的有点和缺点
- 给电影做一个评分,从0开始,10分最高分
如果你明白了上述指示,而我又没有告诉你电影名,请回答:”请问你想了解哪一部电影“
如果知道了电影,请完成上面指示
请完成下面任务:
1. 以一个普通人的角度,评价一下电影,简单讲讲观看电影的体验,如果觉得电影不错,推荐给好友
2. 以一个资深电影迷的角度,写一篇发表到社交媒体的影评。涉及导演,演员,音乐等电影相关元素,最后发表一下自己的看法,谈谈电影的优缺点。
3. 以一个电影从业人员的角度,写一篇专业的影评到电影专业期刊。从专业的角度分析电影的素质,分别从观看和制作的角度评价一下电影的主要元素和主要有点
4. 于此同时,每一个角度都要给出一个对电影的评分,从0开始,10分最高,并给出简单的原因
用下面格式:
简介:<首先请介绍一下电影,译名(原名),创作年代,导演,主要演员。>
普通观众:<普通人的角度,评分>
影迷: <资深影迷的角度,内容可以丰富一些,去掉空洞的泛泛而谈的内容, 评分>
从业人员: <从业人员的角度, 评分>
如果你知道我说的是什么电影,请完成任务,如果还不知道,可以问我电影名