LLM SEO vs traditional SEO

LLM SEO is the work of getting large language models — the systems behind ChatGPT, Perplexity, Google’s AI answers and Chinese assistants such as Doubao, Yuanbao, Kimi and DeepSeek — to mention, recommend and correctly describe your brand when they answer. Traditional SEO is the work of getting your pages to rank and be clicked in a search results list. The two share a foundation of crawlable, accurate, useful content, but they measure success in different places, and some classic SEO habits do nothing for an AI answer.

“LLM SEO” is mostly a search marketer’s label. It describes the same goal as generative engine optimization (GEO) and overlaps heavily with answer engine optimization. The label matters less than the shift it names: the contested position has moved from “which rank” to “is your name in the paragraph the model writes, and is what it says true”.

What changes and what doesn’t

Dimension	Traditional SEO	LLM SEO
What is optimized	The order of results on a search page	What the model says when it answers directly
User action	Clicks a link and lands on a site	Reads the answer, often clicks nothing
Success metric	Rank, clicks, organic traffic	Mention rate, first-recommendation rate, description accuracy
Unit of work	A single page	How consistently the brand is described across the web
What carries weight	Relevance, links, technical health, content quality	Verifiable facts, consistent entity information, citation sources the model trusts
Keyword density	Still part of relevance signals	Carries almost no weight; models quote checkable facts
Feedback loop	Days to weeks; rank is checkable in a tool	Re-test the same question set on a cycle
Data source	Search console reports and site analytics	Archived raw answers from each platform, plus any identifiable referral traffic

What carries over from SEO

More than many vendors admit. Google’s own guidance places its generative search features on top of existing search fundamentals and does not require special AI markup. In practice, the SEO basics that still matter are:

• Accessibility. Pages a crawler cannot reach cannot be read or cited by an AI system either. Check rendering, robots.txt and sitemaps.
• Useful, accurate content. Pages that help a reader finish a task are also the pages a model can quote.
• Structured data. It tells machines what a block is — an FAQ, a product, an organization — without guessing.
• Trust signals. Clear authorship, real evidence and third-party coverage help in both channels (see E-E-A-T).

What LLM SEO adds

• Entity consistency. A model builds its picture of your brand from many sources. If your site, directories and media coverage describe you differently, the answer will be vague or wrong. If your name collides with a better-known company, the name confusion rate has to be fixed before content work pays off.
• Extractable facts. One-sentence definitions, specification tables, explicit numbers and dates, and honest statements of what you are not suited for — answer-first writing rather than long narrative paragraphs.
• Third-party evidence. Models lean on sources beyond your own site, so consistent information in trade directories, media and reference entries becomes part of the work.
• Per-platform measurement. Each platform retrieves and cites differently, so results are recorded per platform rather than averaged into one score.

What does not carry over

Keyword stuffing, thin pages written for search volume and mass-produced content farms do little for an AI answer. A model reuses checkable facts, not term frequency. Likewise, a top ranking does not mean a model will describe you accurately, and one AI mention does not prove you received a visit. Record the two results separately so you do not count the same customer twice.

Which should you choose?

For most companies this is a sequencing question, not an either/or.

• Fix the shared foundation first — crawlable key pages, complete and current facts, working analytics and contact paths. Both channels depend on it.
• Lean toward traditional SEO if your buyers mainly search and click, your category has clear search demand, and your site is not yet technically healthy.
• Lean toward LLM SEO if your buyers consult an AI assistant before shortlisting — common in B2B and export sourcing — or if a baseline shows you are missing or misdescribed in AI answers.
• Budget both by line item: site maintenance, content, evidence checking, platform testing and inquiry follow-up. Do not pay twice for shared work.

Any provider promising a guaranteed top position in AI answers is selling something a generative system cannot deliver. What can be committed to is an auditable process: fixed questions, real platform answers, archived evidence and regular re-tests. To see where you stand, start with the free GEO audit; for the budget side, see what GEO costs and how to scope it.