GEO生成引擎优化是什么？2026年AI搜索双层策略解析

I still remember the first time a user typed "ChatGPT" into our company's "How did you hear about us?" field in our signup form. When I saw that response, I was surprised, but also excited—this was a new opportunity we hadn't yet started to consider. Almost overnight, that user stopped being the exception. People are starting to see charts like this and ask the same questions: Organic ChatGPT referrals are great, but how do we get more of them?

我仍然记得第一次有用户在注册表单的"您是如何了解到我们的？"一栏中填写"ChatGPT"时的情景。看到这个回答时，我感到惊讶，但也非常兴奋——这是一个我们尚未开始考虑的新机遇。几乎一夜之间，这样的用户就不再是个例。人们开始看到类似的图表，并提出相同的问题：来自ChatGPT的自然推荐很棒，但我们如何才能获得更多呢？

It's clearer than ever that AI-driven search is changing how content gets discovered. SEO still matters, but so does optimizing for AI-generated search overviews (like Google's AI Overviews) and AI-native search engines (like ChatGPT Search and Perplexity). This field is still 'unsolved' and a black box—there's no standard 'AI Search Optimization' playbook, and we don't even have a consistent name for the field yet. But after doing quite a bit of research, I wanted to summarize what I've learned so far.

比以往任何时候都更清晰的是，AI驱动的搜索正在改变内容的发现方式。SEO仍然重要，但针对AI生成的搜索概览（如Google的AI概览）和AI原生搜索引擎（如ChatGPT搜索和Perplexity）的优化也同样重要。这个领域仍然是"未解之谜"和一个黑盒——没有标准的"AI搜索优化"指南，我们甚至还没有一个统一的领域名称。但在进行了大量研究之后，我想总结一下我目前的收获。

The Naming Conundrum: What Do We Call It?

SEO (Search Engine Optimization) is now such a mature field that its practitioners even refer to themselves as 'SEOs.' GenAI Search Optimization, on the other hand, is so new that everyone is calling it something different. These names include, but are not limited to:

GEO (Generative Engine Optimization) – The most popular name I've seen.
SGE ('Search Generative Experience') / AI Overviews – Google's terms for their AI-generated search summaries.
LMO (Language Model Optimization) – HubSpot's chosen abbreviation / term.
GAIO (Generative AI Optimization) - I think this one's weird, but some people are trying to make it happen.

SEO（搜索引擎优化）现在已经是一个非常成熟的领域，从业者甚至自称为"SEOs"。而生成式AI搜索优化则如此之新，以至于每个人对它的称呼都不同。这些名称包括但不限于：

GEO（生成引擎优化） – 我见过最流行的名称。

SGE（搜索生成体验）/ AI概览 – Google对其AI生成搜索摘要的术语。

LMO（语言模型优化） – HubSpot选择的缩写/术语。

GAIO（生成式AI优化） - 我觉得这个有点奇怪，但有些人正试图推广它。

Other people just call it 'AI Search', so I'll use that for now. My bet is that the ultimate winner will be 'GEO', which is both broadly inclusive and easy to pronounce. But we'll see!

其他人直接称之为"AI搜索"，所以我暂时也会使用这个说法。我打赌最终的赢家会是"GEO"，因为它既包容又易于发音。但我们拭目以待！

AI Search Optimization: A Two-Layered Problem

To succeed in AI search, you ideally need to be successful in two ways:

Inclusion in Foundation Models: First, your brand needs to be included in the AI model's training data - ideally repeatedly, in a positive light, and in contexts that include relevant phrases and keywords for your topic.
Inclusion in Web Search: Second, when AI searches pull in real-time web data, you want to ideally appear in multiple of the top ~10-15 search results, which may not just be your own site - but also across other top results, particularly sites like Wikipedia and YouTube that are heavily weighted as trusted sources.

要在AI搜索中取得成功，理想情况下你需要在两个方面取得成功：

被纳入基础模型： 首先，你的品牌需要被纳入AI模型的训练数据中——最好是重复地、以积极的形象出现，并且出现在包含你主题相关短语和关键词的上下文中。

被纳入网络搜索： 其次，当AI搜索拉取实时网络数据时，你希望理想情况下出现在前10-15个搜索结果中的多个位置，这可能不仅是你自己的网站，还包括其他顶级结果，特别是像Wikipedia和YouTube这样被高度视为可信来源的网站。

This is an oversimplification, and there are lots of differences and nuances across different models, different AI tools, etc. I'll explain some of these in more detail below.

这是一个过于简化的描述，不同的模型、不同的AI工具之间存在许多差异和细微差别。我将在下面更详细地解释其中一些。

Layer 1: Inclusion in Foundation Models

My favorite read on this topic is from Advanced Web Rankings, who offers a nice breakdown of how to think about getting your content included in LLM training data. This is important because not every user selects 'Search' on ChatGPT, or uses a tool like Perplexity; in most scenarios where users are consulting AI Models, whether in a chat interface or via the API, they're relying on the original corpus of data that the LLM was trained on.

关于这个话题，我最喜欢的阅读材料来自Advanced Web Rankings，他们很好地分析了如何考虑让你的内容被纳入LLM训练数据。这一点很重要，因为并非每个用户都会在ChatGPT上选择"搜索"，或使用像Perplexity这样的工具；在大多数用户咨询AI模型的场景中，无论是在聊天界面还是通过API，他们依赖的都是LLM训练时所使用的原始数据语料库。

Because of that, it's advantageous to act now to maximize your appearance in the sources that new LLMs are being trained on. For example, as Advanced Web Rankings breaks down on their blog, GPT-3 is trained on a mix of the Common Crawl (basically any popular websites), WebText2 (outbound links on Reddit posts with > 3 upvotes), Wikipedia, and books.

因此，现在就采取行动，最大限度地出现在新LLM的训练数据源中是有利的。例如，正如Advanced Web Rankings在其博客中分析的那样，GPT-3的训练数据混合了Common Crawl（基本上是任何热门网站）、WebText2（Reddit帖子中获得超过3个赞的外链）、Wikipedia和书籍。

As a result, helpful ways to optimize for inclusion in foundation models might be:

Having an active Reddit marketing strategy, where your community or support team engage positively and constructively with users, or you post value-added content in relevant subreddits.
Ensuring your brand has a presence on Wikipedia. Do this with caution (Wikipedia editors see straight through blatant marketing).
- For example, when I worked a bit on this for my previous company, Stytch, I didn't add a full Wikipedia page for them (that would be too much!), but I did add them to a few relevant lists and databases (like this one of startup unicorns) so that the company was referenced more often on the site.
Other early GEO practitioners advocate for intentional 'co-occurrence' — ensuring your brand is regularly mentioned using the specific phrases and long-tail keywords that you want your brand to appear alongside.

因此，有助于优化以被纳入基础模型的方法可能包括：

制定积极的Reddit营销策略，让你的社区或支持团队积极、建设性地与用户互动，或者在相关的subreddit中发布有价值的内容。

确保你的品牌在Wikipedia上有存在感。操作时要谨慎（Wikipedia编辑一眼就能看穿赤裸裸的营销）。

例如，当我为之前的公司Stytch做这方面工作时，我没有为他们添加完整的Wikipedia页面（那样太过了！），但我确实将他们添加到了一些相关的列表和数据库中（比如这个独角兽初创公司列表），以便该公司在网站上被更频繁地引用。

其他早期的GEO从业者提倡有意的"共现"——确保你的品牌经常与你希望品牌一同出现的特定短语和长尾关键词一起被提及。

To summarize, it's not fully clear how different foundation models are trained, and which sources are weighted heavily is likely to change and evolve over time. However, in general, more positive appearances for your company across popular web sources — especially user-generated content sites — is helpful to increase your brand's visibility in LLM-generated content (if you're able to establish your presence before the model is trained).

总而言之，目前尚不完全清楚不同的基础模型是如何训练的，哪些数据源权重较高也可能会随着时间的推移而变化和发展。然而，总的来说，你的公司在热门网络资源（尤其是用户生成内容网站）上更频繁地以积极形象出现，有助于提高品牌在LLM生成内容中的可见度（前提是你能在模型训练之前建立存在感）。

Layer 2: Inclusion in Web Search

For AI Search, foundation models are typically then augmented by pulling in real-time web data. In practice, companies typically start by using a web search API call to pull top web results (so, traditional SEO still matters!). These are then filtered and synthesized into an AI Overview with citations.

对于AI搜索，基础模型通常通过拉取实时网络数据来增强。在实践中，公司通常首先使用网络搜索API调用来获取顶级网络结果（因此，传统SEO仍然重要！）。然后，这些结果经过筛选和综合，形成带有引用的AI概览。

This entire space is a black box, but there have been lots of interesting conjecture about what makes it into AI Overviews. Here are a few key themes in what people believe:

Being the top result matters less. Humans almost always click on the top search result. AI doesn't act the same way; the top 10 results on Google aren't necessarily the ones AI overviews pull from. AI overviews synthesize multiple pages and put heavy weight on sources like YouTube, Wikipedia, and LinkedIn, which offer user-generated content.
Good traditional SEO is still important. Google's AI Overviews patent hints at how their process works. The patent references using content from 'Search-result documents' as a key input, so clearly SEO still matters. AI-generated responses use multiple signals beyond just page ranking—things like recency, semantic relevance, and how often a source is linked elsewhere.
On Google, AI Overviews are most common for informational queries. Google's AI overviews ('Search Generative Experience') results are most common for informational keywords, across a mix of low-volume and more general queries.
Don't forget about Bing. ChatGPT search specifically uses Bing and is believed to pull the top ~10-15 results to then synthesize into an overview summary.

整个领域是一个黑盒，但关于什么内容能进入AI概览，已经有了很多有趣的推测。以下是人们观点中的几个关键主题：

成为第一名结果的重要性降低。 人类几乎总是点击排名第一的搜索结果。AI的行为方式不同；Google上的前10名结果不一定是AI概览拉取的内容。AI概览综合多个页面，并高度重视像YouTube、Wikipedia和LinkedIn这样提供用户生成内容的来源。

良好的传统SEO仍然重要。 Google的AI概览专利暗示了其工作原理。该专利提到使用"搜索结果文档"的内容作为关键输入，因此SEO显然仍然重要。AI生成的响应使用多种信号，而不仅仅是页面排名——比如时效性、语义相关性以及一个来源在其他地方被链接的频率。

在Google上，AI概览最常见于信息类查询。 Google的AI概览（"搜索生成体验"）结果最常见于信息类关键词，涵盖低流量和更通用的查询。

别忘了Bing。 ChatGPT搜索明确使用Bing，据信会拉取前10-15个结果，然后综合成概览摘要。

Practical Strategies for Influencing AI Search Results

As we've already covered, this is evolving, and no one totally knows. Keep in mind that:

The relationship between public web presence and foundation model training remains indirect.
Strategies that work today may need to adapt as training methodologies and AI search engines evolve.
Search providers themselves have provided very little direct, public info about their approaches, and there are lots of conflicting opinions out there.

正如我们已经讨论过的，这个领域正在发展，没有人完全了解。请记住：

公开网络存在与基础模型训练之间的关系仍然是间接的。

今天有效的策略可能需要在训练方法和AI搜索引擎发展时进行调整。

搜索提供商本身很少提供关于其方法的直接公开信息，而且外界存在许多相互矛盾的观点。

All of that said, here are my reflections on specific actions that seem to be high value in today's AI Search context (and to broadly improve your company's digital presence and authority):

Get included in sources AI models train on and cite.
- Appearing on reputable, widely cited sites (especially those known for structured, factual content) increases the likelihood of being part of AI-generated answers. Think Reddit and YouTube as good examples.
Focus on co-occurrence.
- While high-ranking pages from your own site matter, AI's keyword and co-occurrence based process means it's important for your brand to be often mentioned next to relevant keywords, across the web. This can be achieved through partnerships, guest blogs, posting on forums, or repurposing content across platforms.
Avoid JavaScript-heavy content.
- AI models favor content they can easily parse, so important text should be in plain HTML. It's also helpful to have structured markup, e.g., FAQ schema, that can be easily retrieved by AI models.
Mentions, not just links.
- AI-generated search results may lead to fewer clicks but more searches—people might not visit individual sites as often if AI provides direct answers. That means that your site being linked is not as critical for GEO as it is for SEO. Since lots of sites punish external links, total mentions on reputable sites, rather than backlinks from them, increases in importance.

尽管如此，以下是我对在当今AI搜索背景下（以及广泛提升公司数字存在和权威性）似乎具有高价值的具体行动的一些思考：

被纳入AI模型训练和引用的数据源。

出现在声誉良好、被广泛引用的网站（特别是那些以结构化、事实性内容著称的网站）上，会增加成为AI生成答案一部分的可能性。Reddit和YouTube就是很好的例子。

专注于共现。

虽然你自己网站的高排名页面很重要，但AI基于关键词和共现的处理过程意味着，你的品牌在整个网络上经常与相关关键词一起被提及非常重要。这可以通过合作伙伴关系、客座博客、在论坛发帖或在多个平台重复利用内容来实现。

避免重度依赖JavaScript的内容。

AI模型偏爱易于解析的内容，因此重要文本应为纯HTML。拥有结构化标记（例如FAQ模式）也很有帮助，AI模型可以轻松检索这些标记。

提及，而不仅仅是链接。

AI生成的搜索结果可能导致点击减少但搜索增多——如果AI提供直接答案，人们可能不会像以前那样频繁访问单个网站。这意味着，你的网站被链接对于GEO来说不像对于SEO那样关键。由于许多网站惩罚外链，因此在信誉良好的网站上的总提及次数，而不仅仅是从它们获得的反向链接，其重要性在增加。

Measuring Success in the Age of AI Search

Companies will need to rethink attribution models and how they monitor for GEO. That starts with adding AI search engines to your self-reported attribution and making sure you're monitoring analytics tools to see how referral traffic from AI tools is trending.

企业需要重新思考归因模型以及如何监控GEO。首先要将AI搜索引擎添加到你的自我报告归因中，并确保你正在监控分析工具，以了解来自AI工具的推荐流量趋势。

There's also a growing crop of 3rd party tools focused on this:

Profound: Founded in 2024, Profound has secured $3.5 million in seed funding to pioneer AI search optimization. Key features include sentiment analysis, competitor benchmarking, and real-time performance tracking.
Scrunch AI: Established in 2022, Scrunch AI raised $4 million in early-stage venture capital funding. It provides tools to evaluate brand visibility, reputation, and accuracy across AI-generated responses.
AthenaHQ – A YCW25 company, AthenaHQ is focused on tracking brand presence across AI-generated responses, especially ChatGPT.
AI Search Grader by HubSpot – Evaluates how often a brand appears in AI-driven search. This is more of a one-off view rather than an actionable monitoring tool, but is an interesting (and free) at-a-glance option.

专注于这一领域的第三方工具也越来越多：

Profound：成立于2024年，[Profound](http://

常见问题（FAQ）

什么是GEO（生成引擎优化）？它与传统SEO有何不同？

GEO是AI搜索优化的新兴领域，优先通过品牌提及、共现策略和可信平台曝光来影响AI生成结果，而非传统链接建设。它需要同时优化基础模型收录和网络搜索可见性。

如何让我的品牌被AI基础模型收录？

需要确保品牌信息以积极形象重复出现在模型的训练数据中，并包含相关关键词和上下文。这通常通过权威平台的内容曝光和品牌提及策略实现。

AI搜索优化为什么要关注Wikipedia等第三方平台？

因为AI搜索会实时抓取网络数据，并高度信任Wikipedia、YouTube等权威来源。在这些平台出现有助于提升在AI搜索结果中的可见性和可信度。