Today’s author with https://seorankmybusiness.com/ shall discuss, how to optimise for LLM visibility or “AI search” rather than just classic Google SEO. These AI platforms may include Perplexity, Gemini, Claudia, Alexa, Siri, ChatGPT, Grok, etc.

Why this matters fpr online marketing

Over the past couple of years, more users are starting their information journey by asking “AI assistants” rather than typing into traditional search engines. These assistants are powered by large-language-models (LLMs) that generate answers based on huge training datasets, not always by crawling and indexing like a traditional search engine. 
What this means for content creators and website owners: being ranked high on Google is still valuable, but you also want your content to be visible to AI-platforms — so that when a user asks the assistant “what’s the best…”, “how do I…”, “who should I use…”, your brand/content is part of that answer.
As one recent article puts it: “LLM SEO = Appear in AI-generated answers.

Key differences: Traditional SEO vs LLM-Visibility

Here’s a short comparison of what to shift in mindset when marketing online:

Traditional SEO LLM / AI-Visibility
Focus on keywords, backlinks, ranking positions in SERPs. Focus on answering conversational queries, being cited / referenced by AI, appearing in answer-snippets.
Mainly webpages pulled by search index. Content must be understandable by the model (clear structure, entities, context) so the model “knows” you.
Tracking keyword rankings, click-throughs from search. Tracking “mentions” or citations in LLM responses (visibility) rather than “rank #1.

One helpful summary: “Why ‘Rankings’ don’t apply to AI search” – because LLMs often don’t ‘rank’ like a search engine crawls real-time, instead they generate answers based on patterns in data.

How to write content for AI visibility

Here are actionable steps and best practices to craft content that gives you a better shot at being surfaced by LLM-based platforms.

1. Understand the query style of AI users

  • Think in terms of conversational questions: “How do I fix X?”, “What is the difference between A vs B?”, “Which tool is best for Y?”

  • Use natural language, as if you’re answering someone directly. This mirrors how users will ask AI platforms.

  • Map out topics your audience will ask, and then build content that directly answers those questions.

2. Choose formats LLMs favour

Some content formats are more likely to be picked up by AI assistants:

  • FAQ style pages: short question + direct answer.

  • How-to guides: walk through steps clearly.

  • Comparison posts (“Tool A vs Tool B”) with clear verdicts.

  • Lists: “Top 5 tools for X”, “10 common mistakes” – such structured formats are easier for the model to parse.

  • Evidence/data driven: original insights, statistics, case studies help signal credibility.

3. Structure content for machine readability

Making content easy for the model (and its retrieval systems) to digest is key. Some tips:

  • Use clear headings (H2/H3) that mirror questions (“What is …?”, “How to …?”).

  • Use bullet points, numbered lists – these create scannable chunks.

  • Use schema markup (FAQ schema, HowTo schema) when relevant so your content is explicitly tagged.

  • Ensure HTML is server-rendered and not buried behind heavy client-side scripting. LLMs (or the retrieval layers feeding them) may struggle with complex JavaScript.

4. Build your topical authority

LLMs and AI-answer engines prefer sources that show depth on a subject (not a one-off blog post). Some practices:

  • Create content clusters: one pillar article + supporting posts that go into subtopics.

  • Link internally between your related posts to show thematic cohesion.

  • Update and refresh your content so it remains current and accurate (which builds trust).

  • Use your brand, tools, case studies, expertise to demonstrate you’re the go-to source.

5. Get external signals and mentions

Even though LLMs don’t “crawl” exactly like search engines, they are trained on huge corpuses that include articles, forums, Q&A sites, etc. Content elsewhere that references you increases your chances of being cited.

  • Participate (or publish) on trusted third-party platforms: major publications, reputable blogs, forums, Q&A sites.

  • Earn mentions in niche communities or industry-specific hubs (Reddit, Quora for example) where conversational questions happen.

  • Make sure your website content is authoritative, unique, not just generic regurgitation.

6. Use the right tone and language

Since you’re writing for humans and for AI readability:

  • Keep language clear, concise, conversational. Avoid unnecessary jargon or overly dense paragraphs.

  • Use entity names, tool names, brands explicitly (e.g., “Tool X”, “Brand Y”) so the model can identify what you’re referring to.

  • Address the user directly (“you want to…”, “here’s how you can…”) rather than passive voice.

  • Provide context: e.g., “In 2025, when searching for cloud-backup tools, users often ask…” — this helps situate the answer.

7. Monitor and measure AI visibility

Since this is a newer channel, you’ll want to keep an eye on how your content is being surfaced by LLMs. Some suggestions:

  • Use tools that track “AI citations” or mentions across platforms like ChatGPT, Gemini, Perplexity.

  • Look at analytics: Are you getting traffic that originates from AI-assistant referrals (if your analytics platform can detect that)?

  • Experiment: change format, update content, add schema, then measure impact on AI visibility.

A simple workflow to get started

Here is a streamlined process you can adopt:

  1. Keyword/Question research – Find the typical questions your audience asks (via forums, Q&A sites, keyword tools).

  2. Map content topics – Choose content cluster topics (pillar + supporting posts) focused on those questions.

  3. Write the pillar article – Use a clear heading structure, schema markup, conversational tone, direct answers.

  4. Create supporting content – Deeper dives, case studies, comparisons, FAQs. Link to the pillar and each other.

  5. Publish and optimise – Check that page loads server-side, headings make sense, schema is implemented, internal links are in place.

  6. Earn external mentions – Share your content in communities, guest post, answer questions on forums, get authoritative citations.

  7. Track visibility and iterate – Monitor AI-mentions and traffic, revise content where needed, refresh annually or as topic evolves.

Common pitfalls to avoid

  • Only chasing keywords and backlinks: If content isn’t easily understood by the model, it may never surface—even if you have strong Google ranking.

  • Writing generic content: If it adds no new value, models may prefer other sources.

  • Neglecting structure or schema: Poor layout makes it harder for AI retrieval systems.

  • Ignoring off-site signals: Without mentions or external validation, you reduce your chance of being surfaced.

  • Treating AI visibility exactly like Google ranking: The methods overlap but are not identical. For example, LLMs are about understanding and knowledge-signals more than just link count.

SEO and LLM Ad-On’s

Writing content for AI-platform visibility is not a replacement for your traditional SEO strategy—it’s an add-on. But in the era where users increasingly ask assistants like ChatGPT, Gemini or Perplexity, you’d be wise to optimise your site and content for how these models “think”.
If you shift your mindset from “rank on Google” to “be visible in AI answers”, you’ll be ahead of many content creators. Structure your content, answer real questions, build authority, and make it easy for the machine and human to understand.