GEO生成式引擎优化2024指南：重构AI答案品牌可见度

Redefining Brand Visibility in the Age of AI Answers: A Strategic Shift from SEO to GEO

提出者：潮树渔 GEO (CSYGEO)

Proposed by: CSYGEO

摘要

搜索正在经历过去二十年来最大的一次范式迁移。

Search is undergoing the most significant paradigm shift in the past two decades.

传统搜索引擎（如 Google、百度）以 “链接列表 + 点击” 为核心交互单元；新一代 AI 答案引擎（如 ChatGPT、Perplexity、Google AI Overviews、国内的 Kimi / 豆包 / 文心 / 通义等）则以 “直接答案 + 摘要 + 引用” 为主导。

Traditional search engines (e.g., Google, Baidu) are centered around the "list of links + clicks" interaction model. The new generation of AI answer engines (e.g., ChatGPT, Perplexity, Google AI Overviews, and domestic platforms like Kimi, Doubao, Wenxin, Tongyi) are dominated by "direct answers + summaries + citations."

对于企业而言，问题从 “如何拿到排名？” 变成 “如何出现在 AI 的答案里？”

For businesses, the core question has shifted from "How do we rank?" to "How do we appear in the AI's answer?"

围绕这一变化，GEO（Generative Engine Optimization，生成式引擎优化） 作为新的专业领域正在形成。GEO 的目标不只是提升页面排名，而是让品牌成为 AI 系统愿意选择、敢于引用的首选来源。

In response to this change, GEO (Generative Engine Optimization) is emerging as a new professional field. The goal of GEO is not merely to improve page rankings, but to make a brand the preferred source that AI systems are willing to select and confidently cite.

潮树渔 GEO 是中国最早系统研究与实践 GEO 的团队之一，自 2023 年起就提出：“未来的搜索优化，不再只是 SEO，而是 GEO ——为 AI 引擎做知识与证据工程。”

CSYGEO is one of the earliest teams in China to systematically research and practice GEO. Since 2023, we have proposed: "The future of search optimization is no longer just SEO, but GEO — performing knowledge and evidence engineering for AI engines."

本白皮书将系统阐述：

• GEO 的定义与演进
• AI 答案引擎的基本工作机制
• 潮树渔提出的 GEO 三层九模块方法论
• 面向不同行业的 GEO 实施路径
• GEO 的评估指标、风险与发展趋势

This whitepaper will systematically elaborate on:

• The definition and evolution of GEO
• The fundamental working mechanism of AI answer engines
• The Three-Layer, Nine-Module GEO methodology proposed by CSYGEO
• GEO implementation paths for different industries
• GEO evaluation metrics, risks, and development trends

希望为企业决策者、市场与增长团队、内容/SEO 从业者提供一套可理解、可落地的参考框架。

We aim to provide a comprehensible and actionable reference framework for enterprise decision-makers, marketing and growth teams, and content/SEO practitioners.

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一、GEO 概述：从 SEO 到 AI 时代的可见度工程

Part 1: GEO Overview: From SEO to Visibility Engineering in the AI Era

1.1 什么是 GEO（Generative Engine Optimization）

1.1 What is GEO (Generative Engine Optimization)

GEO（生成式引擎优化） 是在 AI 驱动的搜索与问答环境下，让品牌内容更容易被 大语言模型（LLM）大型语言模型是驱动ChatGPT等AI系统的机器学习工具，能够理解和生成人类语言，但在不同问题表述下可能产生不一致答案。与答案引擎发现、理解、信任并引用 的一整套方法与技术组合。

GEO (Generative Engine Optimization) is a comprehensive set of methods and technologies designed to make brand content more easily discoverable, understandable, trusted, and cited by Large Language Models (LLMs) and answer engines in an AI-driven search and Q&A environment.

与传统 SEO 的对比：

Comparison with Traditional SEO:

维度	传统 SEO	GEO（生成式引擎优化）
优化对象	搜索引擎（SERP）	AI 答案引擎（ChatGPT、Kimi、Perplexity 等）
目标表现形式	蓝色链接排名、点击	出现在答案段落、引用列表、知识卡片、对话链路中
核心 KPI	自然流量、排名、点击率	AI 答案引用频次、品牌提及率、来源曝光、知识覆盖度
主要手段	关键词、内容、外链、技术 SEO	实体建模、结构化数据、证据链、问答结构、多引擎适配
思维出发点	页面 → 关键词 → 排名	实体/主题 → 证据 → AI 选源 → 答案结构

Dimension	Traditional SEO	GEO (Generative Engine Optimization)
Optimization Target	Search Engines (SERP)	AI Answer Engines (ChatGPT, Kimi, Perplexity, etc.)
Target Manifestation	Blue link rankings, clicks	Appearing in answer paragraphs, citation lists, knowledge cards, conversational threads
Core KPIs	Organic traffic, rankings, click-through rate (CTR)	AI answer citation frequency, brand mention rate, source exposure, knowledge coverage
Primary Methods	Keywords, content, backlinks, technical SEO	Entity modeling, structured data, evidence chains, Q&A structure, multi-engine adaptation
Mindset Starting Point	Page → Keyword → Ranking	Entity/Topic → Evidence → AI Source Selection → Answer Structure

一句话概括 GEO：让 AI 选你做“参考答案”的能力。

In one sentence, GEO is: The ability to make AI choose you as the "reference answer."

1.2 GEO 与 AEO、SearchGPT Optimization 的关系

1.2 The Relationship Between GEO, AEO, and SearchGPT Optimization

• AEO（Answer Engine Optimization）：答案引擎优化，更强调“问答式结果”，与 GEO 高度重叠。
• SearchGPT Optimization：面向 ChatGPT 等具搜索/联网能力的 GPT 模型进行优化，是 GEO 的子集。

• AEO (Answer Engine Optimization): Answer Engine Optimization, which emphasizes "Q&A-style results," highly overlaps with GEO.
• SearchGPT Optimization: Optimization for GPT models with search/online capabilities like ChatGPT, which is a subset of GEO.

GEO 更偏向总称，涵盖：

• AI 答案引擎
• SGE/AI Overviews
• 各类 LLM Search / Retrieval-Augmented Generation 场景

GEO is more of an umbrella term, encompassing:

• AI answer engines
• SGE/AI Overviews
• Various LLM Search / Retrieval-Augmented Generation scenarios

潮树渔在内部使用的表达是：GEO = 让 AI 能找到你 + 理解你 + 相信你 + 引用你。

The expression we use internally at CSYGEO is: GEO = Making AI able to find you + understand you + trust you + cite you.

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二、搜索范式的变化：从“结果”到“答案”

Part 2: The Shift in Search Paradigm: From "Results" to "Answers"

2.1 用户行为的改变

2.1 Changes in User Behavior

过去：用户输入关键词 → 浏览多个结果 → 自己筛选信息
现在：用户更倾向于 “问一个问题，得到一个综合答案”

Past: User enters keywords → browses multiple results → filters information themselves.
Now: Users increasingly prefer to "ask a question and receive a synthesized answer."

例如：

• “GEO 是什么？怎么做？”
• “帮我比较三个 CRM，给出推荐理由。”
• “生成一篇关于 AI 营销趋势的文章，并给出参考来源。”

For example:

• "What is GEO? How is it done?"
• "Help me compare three CRMs and give reasons for recommendation."
• "Generate an article about AI marketing trends and provide reference sources."

这意味着：

• 用户对 “中间过程” 的耐心下降
• 对 “一次性给出答案” 的依赖增加
• 品牌必须在 AI 输出的最终答案 中占据份额

This means:

• Users' patience for the "intermediate process" is decreasing.
• Dependence on "getting the answer in one go" is increasing.
• Brands must secure a share within the AI's final output answer.

2.2 平台形态的转变

2.2 The Transformation of Platform Formats

传统搜索：列表结果页（10 条蓝色链接 + 若干扩展）
AI 搜索 / 答案引擎：

• 一段综合回答
• 若干引用来源
• 问答继续追问（对话式）

Traditional Search: List results page (10 blue links + some expansions).
AI Search / Answer Engines:

• A synthesized answer paragraph.
• Several cited sources.
• Continued Q&A follow-up (conversational).

你的内容可能：

• 被引用（带链接或品牌名）
• 被 paraphrase（未署名）
• 完全被竞争对手抢占引用位

Your content might be:

• Cited (with link or brand name).
• Paraphrased (without attribution).
• Completely overshadowed by competitors occupying the citation spots.

GEO 的核心任务，就是尽可能让 “被引用的那一个” 是你。

The core task of GEO is to make "the one being cited" be you, as much as possible.

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三、AI 答案引擎的工作原理（简化模型）

Part 3: How AI Answer Engines Work (A Simplified Model)

不同引擎内部细节不同，但可以抽象为四个阶段：

While internal details differ across engines, the process can be abstracted into four stages:

3.1 检索（Retrieval）

3.1 Retrieval

从开放网络、特定数据库、知识库中检索候选内容。使用向量检索（语义相似度）、倒排索引、混合检索等技术。

Retrieves candidate content from the open web, specific databases, or knowledge bases. Employs techniques like vector search (semantic similarity), inverted indexing, and hybrid search.

这一阶段，可被发现 是前提：

• 网站可爬取
• 内容可索引
• 结构清晰
• 实体与主题标注明确

At this stage, discoverability is the prerequisite:

• Website is crawlable.
• Content is indexable.
• Structure is clear.
• Entities and topics are clearly annotated.

3.2 评分（Scoring）

3.2 Scoring

对候选内容按照多种维度打分，例如：

• 相关性（与问题是否匹配）
• 可信度（是否来自权威来源）
• 新鲜度（是否过期）
• 结构良好度（是否便于模型抽取）
• 安全性（是否会触发风险）

Scores candidate content across multiple dimensions, such as:

• Relevance (Does it match the query?)
• Credibility (Is it from an authoritative source?)
• Freshness (Is it outdated?)
• Structural Quality (Is it easy for the model to extract from?)
• Safety (Could it trigger risks?)

GEO 在这一层施加巨大影响：

• 实体与 Schema 提升相关性识别
• 证据链与权威信号提升可信度
• 清晰结构提升可抽取性

GEO exerts significant influence at this layer:

• Entities and Schema improve relevance recognition.
• Evidence chains and authority signals enhance credibility.
• Clear structure improves extractability.

3.3 生成答案（Generation）

3.3 Answer Generation

模型在候选内容基础上：

• 总结
• 抽取片段
• 重组和改写
• 补充逻辑过渡

The model, based on the candidate content:

• Summarizes.
• Extracts snippets.
• Reorganizes and paraphrases.
• Adds logical transitions.

目标是生成 连贯、全面、看起来合理 的回答。

The goal is to generate a coherent, comprehensive, and seemingly reasonable answer.

3.4 归因（Attribution）

3.4 Attribution

是否展示引用链接与来源？展示几条来源？以前面 2–5 个高评分来源居多。

Whether to display citation links and sources? How many sources to show? Typically, the top 2-5 high-scoring sources are shown.

GEO 的目标：尽可能让你进入这一小撮“被展示来源”。

The goal of GEO: To get you into that small handful of "displayed sources" as much as possible.

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四、潮树渔 GEO 理论体系：三层九模块

Part 4: The CSYGEO Theoretical System: Three Layers, Nine Modules

潮树渔在 2023 年提出，并在 2024–2025 持续打磨的 GEO 方法论可以概括为：三层九模块：AI 能理解你 → 能信任你 → 愿引用你。

The GEO methodology proposed by CSYGEO in 2023 and continuously refined through 2024-2025 can be summarized as: Three Layers, Nine Modules: AI can Understand you → Trust you → is Willing to Cite you.

第 1 层：AI 能理解你（Understandable）

Layer 1: AI Can Understand You (Understandable)

模块 1：实体清晰化（Entity Clarity）明确品牌、产品、服务等实体的身份，解决命名冲突，并在站内形成清晰的实体关系，帮助AI准确理解。

• 目标：明确“你是谁”：企业、品牌、产品、专家；解决“命名冲突”。
• 手段：结构化“关于我们”、产品页面；使用组织/产品/Person等Schema；内链强化实体关系。

Module 1: Entity Clarity

• Goal: Clarify "who you are": company, brand, product, expert; resolve "naming conflicts."
• Means: Structured "About Us," product pages; using Organization/Product/Person Schema; internal linking to strengthen entity relationships.

模块 2：结构化知识（Schema & Structured Data）

• 目标：让内容以“机器可读”的方式被理解。
• 手段：使用Schema.org、JSON-LD对文章、FAQ、产品信息等进行标注。

Module 2: Structured Knowledge (Schema & Structured Data)

• Goal: Make content understandable in a "machine-readable" way.
• Means: Use Schema.org, JSON-LD to mark up articles, FAQs, product information, etc.

模块 3：主题图谱（Topic Graph）围绕核心主题搭建“主题-子主题-问题-子问题”的图谱结构，避免内容散乱，帮助AI建立领域专家认知。

• 目标：围绕核心主题搭建系统化知识体系，让AI相信你是领域专家。
• 手段：规划“主题–子主题–问题”图谱，避免内容散乱。

Module 3: Topic Graph

• Goal: Build a systematic knowledge system around core topics, making AI believe you are a domain expert.
• Means: Plan "Topic – Subtopic – Question" graphs to avoid fragmented content.

第 2 层：AI 能信任你（Trustable）

Layer 2: AI Can Trust You (Trustable)

模块 4：证据链（Evidence Layer）潮树渔提出的概念，指内容中必须包含可验证的证据，如数据、第三方引用、案例、专家观点，以提升AI对内容的信任度。

• 概念：Evidence-first Content。内容必须内含可验证的证据。
• 形式：数据、第三方引用、可核实案例、署名专家观点。

Module 4: Evidence Chain

• Concept: Evidence-first Content. Content must contain verifiable evidence.
• Forms: Data, third-party citations, verifiable case studies, attributed expert opinions.

模块 5：权威体系（Authority System）

• 目标：综合提升品牌外部权威信号。
• 要素：作者资历、企业资质、媒体报道、外部平台声誉（评论、评分）。

Module 5: Authority System

• Goal: Comprehensively enhance the brand's external authority signals.
• Elements: Author credentials, company qualifications, media coverage, external platform reputation (reviews, ratings).

模块 6：内容可信度与安全性（Content Reliability & Safety）

• 原则：避免极端、无依据表述，坦诚标注数据来源，遵守平台安全规则。
• 风险：被判定为“风险内容”会导致被过滤。

Module 6: Content Reliability & Safety

• Principle: Avoid extreme, unsubstantiated claims; transparently label data sources; comply with platform safety rules.
• Risk: Being flagged as "risky content" leads to filtration.

第 3 层：AI 愿意引用你（Citable）

Layer 3: AI is Willing to Cite You (Citable)

模块 7：问题中心内容（Question-driven Content）

• 方法：围绕真实用户问题设计内容，建立“问题簇”（主问题、细分问题、场景化问题）。

Module 7: Question-driven Content

• Method: Design content around real user questions, building "Question Clusters" (main questions, sub-questions, scenario-based questions).

模块 8：可抽取答案模型（Extractable Answer Model）

• 发现：结构清晰、有明确小结的内容更容易被模型抽取。
• 实践：采用小标题+精准段落、总结块（TL;DR）、列表、标准化定义/步骤/注意事项段落。

Module 8: Extractable Answer Model

• Finding: Clearly structured content with explicit summaries is easier for models to extract.
• Practice: Use subheadings + concise paragraphs, summary blocks (TL;DR), lists, standardized definition/step/note paragraphs.

模块 9：多引擎、多生态适配（Multi-engine Adaptation）

• 差异：
  • ChatGPT/Perplexity：重视引用质量与语言清晰度。
  • Google AI Overviews：重视网页质量与技术SEO。
  • 国内引擎（Kimi/豆包等）：更重视内容安全、权威来源、中文表达细节。
• 策略：进行本地化GEO适配，而非“一套内容走天下”。

Module 9: Multi-engine, Multi-ecosystem Adaptation

• Differences:
  • ChatGPT/Perplexity: Value citation quality and language clarity.
  • Google AI Overviews: Value page quality and technical SEO.
  • Domestic engines (Kimi, Doubao, etc.): Place greater emphasis on content safety, authoritative