8 Ways LLMO (LLM Optimization) Sets Itself Apart from SEO

I remember when “SEO wizard” was the big label everyone chased. But these days, just mastering SEO is no longer enough.

The discussion now covers a wider range of tactics—especially since large language models like ChatGPT, Google Gemini, and Claude are changing how people find and interact with content.

If you’re marketing a product or building a brand, you can’t ignore these trends. You need to plan for both traditional search engines and these newer AI-driven query tools.

That’s when LLMO (Large Language Model Optimization) comes in. Many wonder if sticking to 2019-era SEO methods will still work.

The short answer is no. LLMO is a distinct practice, even though it shares some similarities with the traditional SEO approach.

In the following sections, I outline eight key differences between LLMO and SEO. My goal is to help you adjust your marketing tactics for this AI-focused landscape without relying on guesswork.

Let’s get into the details.

1. Focus on Contextual Relevance

LLM-based search works differently from how Google or Bing delivered results a decade ago.

Users entering a query into an AI might get a single, unified paragraph or a bullet list. LLMs generate an answer by pulling together information from a wide range of online content.

Traditional SEO often centered on targeting specific keywords, but LLMO is more about fitting naturally within the overall conversation.

I’ve observed brands that focus on one keyword while overlooking related terms or similar queries that matter.

If your brand is only recognized for “CRM software” and not also as “customer relationship management solution,” the AI might not include you in its answers.

Some practical tips for achieving contextual relevance:

• Use synonyms and related phrases in your content. For example, combine “CRM” with terms like “customer relationship tool” or “sales pipeline management.”
• Tell layered stories—for instance, describe how your CRM tool addresses scheduling, reporting, and lead scoring—so the AI picks up on multiple aspects.
• Answer related sub-questions from your customers to build richer connections on the topic.

A short example: I once worked with a small software startup that only mentioned “CRM” as if that narrow term said it all.

After we added more detailed content such as “sales pipeline,” “customer data management,” and “marketing automations,” the startup began appearing in AI responses and even saw a modest organic boost from Google. Good content works in all channels.

2. Emphasis on Clear Structure and Formatting

One thing I’ve noticed from reviewing AI-generated content is that LLMs respond well to organized headings, bullet points, and clear formatting.

A big part of how these models work involves processing text in chunks. If your content is disorganized, the AI might struggle unnecessarily.

Try these approaches:

• Use section-based headings. For example, use H2 for main sections and H3 for subsections. This clarity helps the AI process your content.
• Include bullet points. Bullet lists not only help readers but also allow the AI to easily extract and rephrase information.
• Keep sentences concise. While LLMs can handle longer, meandering text, they tend to pick a short, clear quote or snippet when information is presented directly.

For instance, in a blog post on “How to Clean a Grill,” burying the step-by-step instructions in long paragraphs may cause the AI to miss them.

Labeling each step as “Step 1,” “Step 2,” and keeping each one brief makes the content more accessible to both people and AI.

3. Importance of Factual, Verifiable Statements

Large language models sometimes produce inaccurate details because they generate text based on learned patterns.

With time, these models have developed a tendency to trust information that includes verifiable data, especially when multiple sources converge on the same facts.

The idea behind E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) that Google recommends also holds true here.

Some guidelines:

• Cite your sources. If you mention a statistic—say that 70% of your customers do X—explain where that number comes from, such as mentioning a survey of 5,000 customers.
• Write in a straightforward style. Including the study’s year alongside the results (for example, “In 2024, we found that 62% of local contractors prefer software-based scheduling”) helps add credibility.

For one project, I wrote an article on marketing for auto repair shops. I inserted a snippet stating, “According to the National Auto Repair Council, 3 in 4 mechanics prefer customers schedule online.”

That snippet was later referenced by multiple AI outputs. Factual statements tend to work well in AI-generated summaries.

4. Brand Consistency Across Platforms

Imagine your brand is called “Acme Tools.” On your website, you use “Acme Tools.” On LinkedIn, you might be listed as “Acme, Inc.”

On YouTube, your channel is “Acme Tools Official,” and on another platform, the name might be slightly different. That inconsistency can confuse an AI model trying to recognize your brand. LLMs perform best when they see consistent details across the web.

To avoid this problem:

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For example, when I prompted ChatGPT for “email marketing software,” one brand was referred to as “FunnelKing” in one instance and “Funnel King, Co.” in another. That inconsistency muddled the AI’s results. A consistent brand identity across all channels is essential.

5. Optimization for Relevance Over Backlinks

In traditional SEO, pouring effort into inbound links, anchor text, and similar tactics was a primary focus.

In LLMO, however, what matters is how closely your content addresses the user’s complete question.

The AI may sift through vast amounts of text to find the best match. If your site is known mainly for its link profile or domain authority, but not for addressing a specific query, you risk being overlooked by the AI.

Here are some tactics:

• Create sections written in a Q&A style that directly answer user questions. For example, “Can I integrate X with Y? Yes, here is how…”
• Use synonyms around your core service or product.
• Build authority on a topic by creating multiple pages that discuss it in various ways.

A friend who runs a small hair-care brand tried ranking for “argan oil hair serum” by focusing mainly on backlinks to the homepage. Meanwhile, she overlooked an in-depth overview of the science behind argan oil.

In contrast, another brand produced a detailed “Argan Oil 101” post with expert input.

When ChatGPT provided suggestions, the detailed resource was mentioned, while the backlink-heavy page was not. Depth and relevance count more than raw link numbers.

6. Use of Conversational Keywords and Formats

When people interact with tools like ChatGPT or Gemini, they rarely type short phrases such as “Email automation software.” They often ask, “What is the best email automation software for a small e-commerce brand?” The query is more conversational and detailed. This change means that your content also needs to sound natural.

To adjust your content:

• Integrate question-form keywords into your headings. For example, “How do I choose email automation software?” works well as a sub-heading.
• Write in a natural tone, even using a first-person style if it fits your brand.
• Address a variety of angles on the same broad question. Since users might ask the same question in different ways, covering multiple perspectives increases your chances of being recognized.

For illustration, in a post about “cloud-based phone systems,” you could include a bullet Q&A:

Q: Is a virtual PBX reliable for a multi-office business?
A: Often yes, provided you have X, Y, and Z in place.

This clear format increases the chance that a variant of the query will pull from your content.

7. Emphasis on Digital PR and Brand Mentions

Traditional SEO always emphasized building a strong link profile. That remains important, but with LLMO, the focus shifts to how often your brand is mentioned across various reputable contexts.

If AI sees your brand referenced consistently when discussing “top accounting software for freelancers” on various credible sites, it tends to favor your brand for that query.

Here are some steps to consider:

• Reach out to well-known blogs or industry sites with news about your product or insights.
• Team up with professional organizations that sponsor events or create industry lists.
• Encourage discussions about your product on forums like Reddit or Quora, as these extra mentions can be noticed by AI models.

In one case, a brand encouraged its users to share unboxing experiences across several forums.

Soon after, the brand’s name appeared more frequently. When I later tested ChatGPT for a list of “top budget-friendly 3D printers,” that brand showed up in the suggestions.

Regular brand mentions on authoritative sites can boost your profile.

8. Consideration of AI Training Data

There is one drawback to using AI chat services: the data they use is frozen in time.

For example, ChatGPT might state that its information is only current up to 2021.

Other models may update more often but still only cycle through training periodically. If your brand appears only recently or if you rebrand, earlier references may still dominate the AI’s responses.

Some proactive moves include:

1. Keeping your new brand or product name consistent across all online mentions.
2. Continuously securing external references so the next training cycle includes your current brand.
3. Paying attention to setups that use retrieval-augmented generation if you plan to integrate your brand into custom AI solutions.
4. Monitoring how your brand appears in AI chat results, especially after major updates from the developers.

One example involved a B2B startup that rebranded from “SoftMechanic” to “SoftMech” but left many earlier mentions intact. For several months, the AI continued recommending the old name. Keeping a single, consistent name can avoid this kind of confusion.