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AI Search Fundamentals更新于

LLM SEO

又称: LLM optimization, SEO for LLMs, LLMO

一句话解释

LLM SEO is the practice of influencing what large language models say about your brand — getting named in their answers and cited in their sources. It is a synonym for generative engine optimization, emphasising the model rather than the product built on top of it.

Training data versus retrieval

The most common misconception about LLM SEO is that the goal is getting into the training data. For current consumer products, it usually is not.

Models have a knowledge cutoff and are frozen after training. What is not frozen is retrieval: when a user asks a question, the product searches the live web, reads a handful of pages, and grounds its answer in what it found — see grounding. That retrieval step happens fresh on every question, which is the surface you can actually influence this quarter.

Training data still matters for what a model believes about a well-established brand with no retrieval. For everyone else, retrieval is the game.

What influences a retrieved answer

  • Being reachable. If AI crawlers are blocked, you cannot be retrieved at all.
  • Being retrievable. Pages that answer a specific question directly are the ones that come back for it.
  • Being quotable. Engines lift passages. A self-contained answer under a question-shaped heading is what gets used.
  • Being named elsewhere. Models name brands they see named on the sources they read, which is why third-party list placement moves this more than almost anything on your own site.

Why this term is worth knowing

LLM SEO, AI SEO, GEO and AEO are largely the same discipline under four labels. The vocabulary has not settled and probably will not for a while. What matters is that the work underneath is consistent: make yourself readable, quotable, and named.

常见问题

Can you optimize a model's training data?
Not directly, and anyone claiming to is overselling. What you can influence is what the model retrieves at answer time. Most consumer AI products now search the live web before answering, so the practical target is the retrieved sources, not the training corpus.
Is LLM SEO different from GEO?
They describe the same work. GEO is the more widely used term; LLM SEO and LLMO are variants that emphasise the model. Choosing between them is a vocabulary decision, not a strategic one.
How do you know if LLM SEO is working?
Ask the models the questions your customers ask, on a fixed schedule, and record two things: whether the answer names your brand and whether its citations include your domain. Single answers vary run to run, so read the rates across several runs rather than any one response.

相关术语

  • Generative engine optimization (GEO)

    Generative engine optimization (GEO) is the practice of getting a brand named and cited inside answers produced by AI engines such as ChatGPT, Gemini, Perplexity and Google's AI Overviews. Where SEO competes for a position in a ranked list of links, GEO competes for a place inside a single synthesized answer.

  • AI SEO

    AI SEO is used for two different practices. The first is optimizing so your brand appears in AI-generated answers — the same work as generative engine optimization. The second is using AI tools to produce SEO work faster. They share a name and almost nothing else.

  • ChatGPT SEO

    ChatGPT SEO is the practice of getting a brand named and cited in ChatGPT's answers. Because ChatGPT searches the live web before answering most commercial questions, the practical target is the pages it retrieves and the third-party sources that name you — not the model's training data.

  • Grounding (AI)

    Grounding is the practice of connecting a language model's answer to specific external sources retrieved at answer time, rather than relying on what the model memorised during training. A grounded answer can cite where each claim came from, which is what makes citations — and therefore AI visibility — possible at all.

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