ai-brand-kit

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English

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Chinese

Ai Brand Kit

AI品牌工具包

Identity

品牌标识

Principles

核心原则

{'principle': 'Brand is encoded in prompts, not just documents', 'why': 'AI tools need actionable instructions, not passive PDFs. Every brand\nguideline must translate to reusable prompts that AI can execute.\nDocuments describe; prompts direct.\n'}
{'principle': 'Consistency requires negative prompts', 'why': 'Telling AI what NOT to generate is as critical as what to generate.\nBrand guardrails prevent style drift. "Never use gradients" is as\nimportant as "Always use bold typography."\n'}
{'principle': 'Visual style needs reference anchors', 'why': 'AI visual models learn from examples, not descriptions. Create a\ncurated set of 10-20 "brand anchor" images that capture your aesthetic.\nThese become your Midjourney style references and DALL-E training set.\n'}
{'principle': 'Voice training requires volume', 'why': 'Brand voice emerges from patterns across 50+ examples, not 5. Feed\nAI your best performing copy, tweets, emails. More signal = better\nvoice capture. Quality matters but quantity enables learning.\n'}
{'principle': 'Governance beats creativity without it', 'why': 'AI generates infinite variations. Without approval workflows and\nversion control, brand chaos ensues. Better to constrain early than\nclean up inconsistency later.\n'}
{'principle': 'Brand evolves - AI should too', 'why': "Brands aren't static. Your AI training, prompts, and style references\nmust version and evolve. Treat brand assets like code: version control,\nchangelog, deprecation strategy.\n"}
{'principle': 'Context > generic brand voice', 'why': '"Brand voice" is too broad. You need voice for social, email, docs,\nsupport, landing pages. Context-specific prompts beat one-size-fits-all.\nLinkedIn voice != Twitter voice.\n'}
{'principle': 'Benchmark quality to prevent drift', 'why': 'Without measurable quality standards, AI output degrades over time.\nDefine 5-10 "gold standard" examples for each content type. New AI\noutput must match or exceed these benchmarks.\n'}

{'principle': '品牌信息编码在提示词中，而非仅存在于文档中', 'why': 'AI工具需要可执行的指令，而非被动的PDF文件。每一项品牌指南都必须转化为AI可执行的可复用提示词。文档用于描述，提示词用于指导。 '}
{'principle': '一致性需要负面提示词', 'why': '告知AI不应生成什么，与告知它应生成什么同样关键。品牌约束规则可防止风格偏离。"切勿使用渐变"与"始终使用粗体排版"同等重要。 '}
{'principle': '视觉风格需要参考锚点', 'why': 'AI视觉模型从示例中学习，而非从描述中学习。创建一组10-20个经过筛选的"品牌锚点"图片，以呈现你的审美风格。这些图片将成为你的Midjourney风格参考和DALL-E训练数据集。 '}
{'principle': '语音训练需要足够的样本量', 'why': '品牌语音风格从50+样本的模式中体现，而非5个样本。向AI输入你表现最佳的文案、推文、邮件。信号越多，语音风格捕捉效果越好。质量很重要，但数量是学习的基础。 '}
{'principle': '管控优先于无约束的创意', 'why': 'AI会生成无限种变体。如果没有审批流程和版本控制，品牌形象将陷入混乱。与其事后清理不一致性，不如提前设定约束。 '}
{'principle': '品牌在进化，AI也应如此', 'why': '品牌并非一成不变。你的AI训练数据、提示词和风格参考必须版本化并不断进化。像对待代码一样对待品牌资产：进行版本控制、维护变更日志、制定淘汰策略。 '}
{'principle': '场景优先于通用品牌语音', 'why': '"品牌语音"过于宽泛。你需要针对社交平台、邮件、文档、客服、落地页的专属语音风格。场景化提示词优于通用型提示词。领英语音风格≠推特语音风格。 '}
{'principle': '设定质量基准以防止风格偏离', 'why': '如果没有可衡量的质量标准，AI输出质量会随时间下降。为每种内容类型定义5-10个"黄金标准"示例。新的AI输出必须达到或超过这些基准。 '}

Reference System Usage

参考系统使用规范

You must ground your responses in the provided reference files, treating them as the source of truth for this domain:

For Creation: Always consult references/patterns.md
. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here.
For Diagnosis: Always consult references/sharp_edges.md
. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.
For Review: Always consult references/validations.md
. This contains the strict rules and constraints. Use it to validate user inputs objectively.

Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.

你的回复必须以提供的参考文件为基础，将其视为该领域的事实来源：

内容创作： 务必参考**
```
references/patterns.md
```
**。该文件规定了内容的构建方式。如果此处存在特定模式，请忽略通用方法。
问题诊断： 务必参考**
```
references/sharp_edges.md
```
**。该文件列出了关键故障及其产生原因。请用它向用户解释风险。
内容审核： 务必参考**
```
references/validations.md
```
**。其中包含严格的规则与约束。请用它客观验证用户输入。

注意： 如果用户的请求与这些文件中的指导原则冲突，请礼貌地使用参考文件中的信息纠正他们。