What is LLM SEO? How to Optimise for Large Language Models

LLM SEO is the practice of optimising your brand, content, and digital presence so that large language models (LLMs) accurately represent, cite, and recommend you in their responses.

Where traditional SEO optimises for a ranked position in Google’s list of links, LLM SEO optimises for how your brand appears inside a generated answer. When a buyer asks ChatGPT “what type of flooring is best for underfloor heating?”, or asks Perplexity “who are the best outdoor furniture retailers in the UK?”, LLM SEO is the discipline that determines whether your brand appears in the answer, how accurately it is described, and whether it is recommended.

The term is a slight misnomer: large language models are not search engines in the traditional sense. But “LLM SEO” has become the widely used shorthand for this discipline, and it is the term people reach for when they start researching it. The underlying practice is better described as making your brand visible, accurate, and citable inside AI-generated responses, which is why it also overlaps with related terms: Generative Engine Optimisation (GEO), Answer Engine Optimisation (AEO), and AI search optimisation. LLM SEO is the broadest framing, covering any work that improves how large language models understand and represent your brand.

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Why LLM SEO is a distinct discipline from traditional SEO

Traditional SEO rests on a well-established model: Google crawls pages, indexes them, and ranks them by relevance and authority signals (primarily content quality and backlinks). You optimise for a ranked position. The user clicks a link. You receive traffic.

LLMs work differently. When a user asks ChatGPT or Gemini a question, the model does not retrieve a list of ranked links. It generates an answer by drawing on:

• Training data: The corpus of text the model was trained on, which includes websites, published articles, Wikipedia, and structured databases
• Live retrieval (where enabled): For tools like Perplexity and SearchGPT, real-time web content is retrieved and synthesised into the response
• Structured knowledge signals: Wikidata entries, schema.org markup, and other machine-readable data sources that help models build entity understanding

A brand that ranks well on Google may be completely absent from LLM responses if its content is not structured for extraction, its entity signals are inconsistent, or it lacks credible third-party references. Conversely, a brand with clear, specific, well-structured content and strong entity signals can appear frequently in AI responses even before it has significant Google rankings.

This is the fundamental argument for treating LLM SEO as a separate workstream: the signals that drive AI visibility are different from the signals that drive search rankings, and optimising for one does not automatically optimise for the other.

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How LLMs decide what to include in a response

Understanding what drives LLM SEO starts with understanding how a large language model builds a response about a brand or category.

Training data representation

For models that generate from training data rather than live retrieval, a brand’s visibility in AI responses correlates with how well-represented that brand is in the training corpus. This includes the brand’s own website content, but also coverage across industry publications, directories, review platforms, and structured data sources.

Brands that are frequently mentioned in high-quality, authoritative sources are more likely to be surfaced by models trained on that data. This is why third-party coverage matters for LLM SEO beyond its traditional backlink value: a retailer mentioned in Retail Gazette, listed in the BIRA directory, or featured in an IMRG report is building the kind of signal AI models use to decide who is worth including in a response.

Entity clarity

A model can only reliably represent a brand if it has a consistent, unambiguous understanding of what that brand is. This means:

• The company name is consistent across all mentions (not “Acme Tiles” in one place and “Acme Tiles Ltd” in another)
• The core product range is described specifically: “porcelain and ceramic floor and wall tiles for residential and commercial projects” rather than “quality tiles for every room”
• The geography served, product categories stocked, and customer type are stated explicitly

Many retailers have inconsistent descriptions across their website homepage, Google Business Profile, Trustpilot listing, and trade directory entries. Each inconsistency is a signal that makes it harder for an AI model to confidently represent the brand.

Inconsistent entity signals produce confused AI representations. A model uncertain about what a brand does will either omit it or describe it inaccurately.

Structured and extractable content

LLMs synthesise information from source content. Content that is explicit, specific, and directly answers questions is more extractable than narrative prose that buries the answer in paragraph five.

For LLM SEO, this means:

• Opening paragraphs that directly answer the question the page is targeting
• FAQ sections with question-shaped H2 and H3 headings followed by complete standalone answers
• Definitional “what is” content that names the brand, its category, and its specific capabilities
• Case study content with named, specific metrics rather than vague claims

Third-party authority

AI models weight sources that are frequently cited, authoritative, and consistent. A brand mentioned only on its own website with no third-party references is a weak signal. A brand cited across industry publications, listed on review platforms, referenced in awards shortlists, and included in structured databases (including Wikidata) is a much stronger signal.

This mirrors the backlink logic of traditional SEO but with a different mechanism. In LLM SEO, the goal is citation density across authoritative sources, not link equity.

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LLM SEO vs GEO vs AEO: what is the difference?

These terms are used differently by different practitioners. Here is a working distinction:

Term	Focus	Primary surfaces
LLM SEO	Broad: any optimisation for how LLMs represent a brand	All AI tools using LLMs
GEO (Generative Engine Optimisation)	How brands appear in AI-generated answers and content	ChatGPT, Gemini, Perplexity, Claude
AEO (Answer Engine Optimisation)	How brands appear in AI-assisted answer features inside search	Google AI Overviews, Bing Copilot, People Also Ask

In practice, the underlying work is largely the same across all three: structured content, entity clarity, third-party authority, and schema markup. The distinction matters mainly for measurement and priority: a brand focused on Google AI Overviews will weight AEO work differently from a brand focused on ChatGPT and Perplexity citation.

For most brands, LLM SEO is the useful umbrella term because it captures the full scope of the problem without requiring separate workstreams for each AI surface.

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The five pillars of LLM SEO

1. Entity establishment

Before any other LLM SEO work, a brand needs a consistent, machine-readable entity. For a retailer this means:

• Consistent trading name, address, and description across Google Business Profile, Trustpilot, trade directories, and the website
• Organisation schema (schema.org/Organization) on the website homepage with company name, URL, logo, description, and geography
• A clear, specific “About” page that names the product categories stocked, the geography served, and the type of customer you serve
• A Wikidata entry if you are an established brand (larger retailers and brands with meaningful history are worth the effort)

Entity establishment is foundational. Without it, all other LLM SEO work is building on inconsistent signals.

2. Structured, answer-first content

LLMs extract answers from source content. Every important page on a site should open with a direct, specific answer to the question it is targeting. The “inverted pyramid” writing structure (conclusion first, then supporting detail) is the LLM SEO default.

FAQ sections are among the most LLM-friendly content formats. A FAQ section with ten question-shaped H3 headings, each followed by a complete standalone paragraph, is a high-value LLM SEO asset. Each FAQ answer is a directly citable unit of content.

3. Third-party citation signals

For retailers, the relevant third-party signals are different from what a software company would target. The sources AI models draw on when answering questions about retail brands include:

• Customer review platforms: Trustpilot, Feefo, and Google Reviews. High volumes of specific, detailed reviews make a brand more citable and more accurately represented.
• Trade press: Coverage in Retail Gazette, Drapers, Internet Retailing, and the Retail Technology Innovation Hub carries strong authority signals for retail category queries.
• Trade associations: BIRA, IMRG, and British Retail Consortium directories and publications are trusted sources that AI models weight accordingly.
• Supplier and brand partner pages: If you stock well-known brands, having your name appear on their “stockist” or “where to buy” pages builds retailer-specific citation signals.
• Local and regional press: For independent and regional retailers, local news coverage and business directories contribute meaningfully to entity signals.
• Product and category awards: Industry award shortlists and wins (Retail Week Awards, Drapers Awards, IMRG awards) are highly citable third-party validations.

Building this citation base is a deliberate programme of work, not a by-product of other activity.

4. Schema and structured data

Schema.org markup in JSON-LD format makes brand and content signals machine-readable in a form that AI crawlers and retrieval systems can process reliably. For LLM SEO, the most valuable schema types are:

• Organization: Company name, URL, logo, description, founding date, geography
• FAQPage: Marks up question-and-answer content explicitly
• Article / BlogPosting: Marks up authored content with date, author, and topic
• Product: For product-led businesses, marks up specific products with attributes

Schema is underused for LLM SEO despite being well-understood from traditional SEO. The implementation effort is low relative to the signal value.

5. Content freshness

AI tools with live retrieval weight recently published and updated content. Regular publishing of specific, structured content maintains retrieval frequency. For brands in fast-moving categories, letting content go stale is a GEO risk, not just an SEO risk.

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Why LLM SEO matters most for retailers right now

Retailers face a specific version of the LLM SEO problem, and it is more urgent than for most other business types.

Product research has moved into AI tools faster than almost any other category of query. A buyer deciding between carpet types, comparing outdoor furniture ranges, or looking for a tile supplier asks ChatGPT or Perplexity before they open Google. They get a generated answer, often with named brands and specific recommendations, and they start there. If your brand is not in that answer, you are not in the consideration set.

The challenge for most retailers is that their digital presence was not built with AI citation in mind. Product descriptions are supplier copy, often identical across competing stockists. Category content is thin or absent. Brand descriptions on Google Business Profile, Trustpilot, and the website do not match. AI models are left with weak, inconsistent signals and default to the brands with the clearest, most consistent, most frequently cited presence.

For retailers, LLM SEO work centres on two high-value areas:

Product content structure. AI-generated answers about product categories draw on the most specific, attribute-rich content available. Product descriptions that include material, dimensions, use case, and specific properties are more citable than generic marketing copy or supplier-provided descriptions. Retailers whose product content is specific and attribute-level benefit directly, because their pages become the source AI models extract answers from when buyers ask category questions.

Category authority content. Retailers who publish clear, specific buying guides and category explainers (“what to look for when buying luxury vinyl tile”, “the difference between porcelain and ceramic tiles”) give AI models exactly the kind of citable content buyers are asking about. This content improves both LLM SEO citation and traditional organic rankings, making it among the highest-ROI content investments for any retailer.

For retailers using AI content platforms, the quality of the underlying AI-generated product content directly affects LLM SEO performance. Product descriptions that are structured, specific, and schema-compliant are more citable than generic AI output. This is one of the ways that structured AI merchandising differs from generic AI copywriting in its impact on brand visibility.

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How to measure LLM SEO performance

Measurement is the hardest part of LLM SEO. There is no equivalent of Google Search Console that shows you citation frequency across AI tools. Current best practice:

Query testing: Define a set of 10-20 queries your target buyers are likely to ask AI tools. Run them monthly across ChatGPT, Perplexity, Google AI Overviews, and Claude. Record whether you appear, how you are described, and who appears when you do not. This is manual but it is the only reliable signal available today.

Emerging tools: Ahrefs Brand Radar (Advanced plan) and a handful of specialist GEO monitoring tools are building AI citation tracking. This space is developing quickly.

GSC impression data: While not a direct LLM measure, GSC impressions for informational queries (what is, how to, best X for Y) indicate whether content is being picked up for featured snippets and AI Overviews, which are proxies for LLM SEO performance.

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Getting started with LLM SEO

The practical starting point for most brands is an audit of current AI brand visibility:

1. Search your brand name in ChatGPT, Perplexity, and Google AI Overviews. Is the description accurate and complete?
2. Search the category questions your buyers ask. Do you appear? Who does?
3. Check your Wikidata entry exists and is accurate
4. Review Organisation schema on your homepage
5. Identify your five most important FAQ answers and check they are formatted as standalone extractable content

These five steps take under an hour and will surface the highest-priority LLM SEO gaps in any brand’s current digital presence.

If you want a structured LLM SEO audit and implementation plan for your business, book a conversation with the merchi.ai team.

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