If your brand isn't being mentioned in AI responses , you're losing visibility in the new marketplace: LLMs . This article is my roadmap—the one I use daily—for positioning yourself in LLMs (LLMO/GEO) and increasing mentions in search engines like Perplexity, Copilot, and Google's generative summaries.
I'm Christian, Head of SEO at Línea Gráfica, an eCommerce agency . Today I want to share 7 proven tips to improve your eCommerce visibility and mentions across different LLMs. The idea is simple: to ensure that any LLM understands your expertise , can confidently reference you, and, most importantly, that this impact is measurable and repeatable . We'll start with what makes the biggest difference: citations and statistics that models actually want to use.
1) Citations and statistics: how to choose sources that LLMs actually reference
LLMs love evidence-based content. If your pages include recent data and clear citations, the likelihood of a RAG-based engine (e.g., Perplexity or Copilot) selecting you as a canonical source increases. I approach this with three practical rules:
- Freshness and traceability. Prioritize studies from the last 12–18 months, including author, date, and method. When citing, always place the source near the data point (not at the end of the post), because LLMs extract context from fragments.
- Thematic authority and diversity. It blends papers/organizations (universities, regulators, institutes), sector reports, and proprietary data. In my case, when I tried including a mini internal study (sample, methodology, limitations, and simple graphs), I started seeing more mentions in generative summaries for specific long-tail keywords.
- Use a format that facilitates scraping. Avoid "images with text." Use HTML tables, ordered lists, and subheadings that contain the exact question that the data answers.
2) “Cite your sources!”: formats, location and practical examples
It's worth repeating because it makes all the difference: Cite your sources! And the more recent the sources, the better. I use two patterns that LLMs "read" very well:
Pattern 1 — Short contextual quote.
“63% of X… — Source: Y Report (2025)”
Place it immediately after the data. If you use links, add a descriptive anchor (not "here").
Pattern 2 — End-of-section reference block.
A mini-bibliography for each section, not just at the end of the article. This creates multiple semantic anchor points.
Where to put the links:
- Along with the data.
- In subheading H3, if you summarize a question.
- In callouts when the source "is an authority" on the subject.
Personal tip: When optimizing key pieces, I include 2–3 fresh quotes and 1 evergreen (guides or papers that don't become outdated quickly). And, if the site has other related pieces, I link to them with anchor text that reinforces the entity (“SEO market research 2025”, “LLMO glossary”, etc.). This well-named interlinking helps the model understand your area of expertise.
3) Technical terms (without losing clarity): script to sound expert and conversational
LLMs recognize expert terminology… but they also penalize opacity. My style guide:
First the answer, then the precision. I start each section with a simple sentence that anyone can understand. Then I introduce the correct technical term (e.g., RAG, embeddings, anchors, entity, LLMO, GEO).
Definition of 1 line. “RAG = retrieve external content to enrich the model's response”.
Minimal example. “If a user asks X, our paragraph with figures and link Y is what the engine retrieves.”
Sidebar glossary. In large projects, I maintain a reusable glossary. When I did this, I noticed greater consistency in how we were cited for related topics.
The phrase I usually use is: “Use technical terms when applicable.” And I add: “Explain them as if you were telling a customer in 20 seconds.”
4) Semantic research with DinoRank: breadth, intent, and clusters that do rank (my stack)
It's no secret: I love DinoRank . For pieces seeking visibility in LLMs, I use it like this:
Keyword Research + semantic breadth
I map related entities and long-tail keywords with informational/mixed intent. With the new Keyword Research module, I generate clusters and prioritize topics with real opportunity (volume + gap + affinity to the site's entity).
DinoBrain for content that ranks, not filler.
I use it as a specialized SEO co-pilot (not as a copywriter). It gives me angles and points of evidence to respond better than the competition. “DinoBrain is brutal” for this — and yes, I say that a lot because I see it in real projects.
DinoRank Copilot for SEO questions and outlines
When things get complicated (e.g., how to model a How-To vs. FAQ in a specific category), Dinorank Copilot —trained by top SEOs—helps me solve it in minutes. The result is a more robust outline and consistent markup.
Practical results for clients: better entity consistency between pieces, more exact matches with user queries, and content that LLMs understand at a glance.
5) Smart JSON-LD: FAQ + How-To + Article to gain coverage
If there's one thing I've noticed that makes life easier for LLMs, it's good structured markup. My basics:
Article Schema
headline, author, datePublished, dateModified, about (list of subject entities), citation (yes, you can include formal references).
FAQPage Schema
Clear question, brief answer (2–3 sentences) and, if applicable, a link to the section of the article that expands on it.
How-To outline (if applicable)
Steps with verbs in the infinitive, materials/tools, duration.
Always validate using Rich Results Test and Schema Markup Validator. I mark up FAQs with the actual questions I see in long-tail keywords; when I started doing this, I noticed that some LLMs were literally using my Q&As to answer them.
Example template (FAQPage)
<script type="application/ld+json">
{
"@context":"https://schema.org",
"@type:FAQPage",
"mainEntity":[
{
"@typeQuestion",
"nameWhat is LLMO and how does it differ from traditional SEO?",
"acceptedAnswer":{
"@type":"Answer",
"LLMO focuses on appearing as a source in AI-generated answers. SEO works on ranking in search results pages. Both share an on-page/off-page foundation, but LLMO prioritizes citations, entity, and structured data."
}
},
{
"@typeQuestion",
"nameWhich JSON-LD schemes are most helpful for LLMs?",
"acceptedAnswer":{
"@type":"Answer",
"text: FAQPage, Article, and HowTo. Use them with real questions, short answers, clear dates, and, if applicable, the citation property in Article."
}
}
]
}
</script>
6) Structure that LLMs love: H2/H3, lists and direct answers
Page architecture is crucial. My favorite pattern:
- Unique title (H1) with the clear promise.
- H2 per objective (each H2 corresponds to a specific intention).
- H3 as exact user questions.
- Numbered lists for processes and tables for data.
- A “Short Answer” block of 2–3 lines at the beginning of each section.
When I tested the "answer first" approach, I found that search engines extracted the key paragraphs more easily (and therefore, more mention options). And if you add FAQs at the end of each H2 heading, you address common questions and create material that LLMs love to reuse.
- Lightning template for an H2 section
- Short answer: 2 lines with the conclusion.
- Evidence: 1 fact or example + quote.
- How to apply it: numbered steps.
- Short FAQ: 1–2 questions with 2-sentence answers.
7) Measure and optimize your generative visibility: audit and testing by engine
What isn't measured, doesn't improve. I conduct a monthly audit focused on LLMs:
- Blind tests in Perplexity/Copilot/AI Overviews with your target queries: Are you cited? What snippet do they extract?
- Intention coverage matrix: information, comparisons, how-to, soft transactional.
- Mapping gaps: questions you haven't yet answered on any URL.
- Strengthen entity: add 1–2 pillar pieces and 3–4 satellites with semantic interlinking.
- Quarterly updates of figures and graphs (remember: date visible).
In my case, when I added flagged FAQs, remade internal anchors and updated figures in 3 key posts, mentions in LLM answers increased for brand + generic queries (“best tools for…”, “guide to…”).
If I had to choose one takeaway, it would be this: LLMs reward clear evidence within a clean architecture . It's not magic; it's a process. My seven tips work because they align recent citations , structure H2/H3 headings in a question-like format , use JSON-LD , and focus on thematic entity around what you actually want to be cited for.
