Learn about AI search optimization (GEO, AEO, LLMO) with free, reliable guides and resources, from the fundamentals, to technical configurations, content optimization, measurement and tools.
9. Optimize for AI Search (GEO, AEO, LLMO)
Tips to Optimize for AI Search
There is much talk about being mentioned by the websites included in the training models. And it is correct. However, the following phrase should be added to it: "and be sure to be mentioned in the correct semantic context".
Being mentioned is good; being mentioned in a "chunk" that is semantically relevant to your business, services and products offering is 100% better.
Another tip: When extracting the query fan-outs, especially the ones of conversational searches where your competitors are appearing and you are not, do not make the mistake of immediately creating content or chunks trying to target those queries.
Before use them to review your architecture, and look at the contents of your website that already try to answer those questions, and identify the content gaps your website has vs. your competitors.
Set the bases to own the topic in all its facets, and then go on the micro-level of optimizing each piece of content.
Being mentioned is good; being mentioned in a "chunk" that is semantically relevant to your business, services and products offering is 100% better.
Another tip: When extracting the query fan-outs, especially the ones of conversational searches where your competitors are appearing and you are not, do not make the mistake of immediately creating content or chunks trying to target those queries.
Before use them to review your architecture, and look at the contents of your website that already try to answer those questions, and identify the content gaps your website has vs. your competitors.
Set the bases to own the topic in all its facets, and then go on the micro-level of optimizing each piece of content.
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One of the low hanging fruits for relatively newer brands: publish conquest content
New brand vs established brand A
New brand vs established brand B
and so on
LLMs love content that helps them make sense of newer entities
Don’t stop at text comparisons
Repurpose the content as infographics, long form video, shorts
Distribute via 3rd party sites, influencers, podcast interviews
New brand vs established brand A
New brand vs established brand B
and so on
LLMs love content that helps them make sense of newer entities
Don’t stop at text comparisons
Repurpose the content as infographics, long form video, shorts
Distribute via 3rd party sites, influencers, podcast interviews
💁🏻♂️ Communicate your core offering with clarity and conciseness.
💁🏻♂️ Replace marketing copywriting with almost boring declarative copy.
💁🏻♂️ Literally call out who your product/service is for.
💁🏻♂️ Dominate the definition layer first (via informational pages), then connect these to commercial pages via schema markup and internal links. This is because definition-based content has better opportunity to be included in AI outputs while commercial pages are critical for relevance, grounding, and authority once the definition or discovery intent is satisfied. For example, an LLM might define “headless cms” using your blog post, then mention or cite you from your product page.
And you can tie this together using schema markup, referencing individual chunks by assigning it with a unique ID, then defining the information via a claim item property.
💁🏻♂️ Actually have a good product 🤣
💁🏻♂️ Replace marketing copywriting with almost boring declarative copy.
💁🏻♂️ Literally call out who your product/service is for.
💁🏻♂️ Dominate the definition layer first (via informational pages), then connect these to commercial pages via schema markup and internal links. This is because definition-based content has better opportunity to be included in AI outputs while commercial pages are critical for relevance, grounding, and authority once the definition or discovery intent is satisfied. For example, an LLM might define “headless cms” using your blog post, then mention or cite you from your product page.
And you can tie this together using schema markup, referencing individual chunks by assigning it with a unique ID, then defining the information via a claim item property.
💁🏻♂️ Actually have a good product 🤣
Reviews are AI rocket fuel for local answers. Prompt customers to leave detail-rich reviews that name your niche, service, or vibe (“scaled our Erie shop’s SEO,” “kid-friendly patio”). Then surface them with 'Review' and 'AggregateRating' schema on your site. Those grounded, third party signals make LLMs trust you for hyper-specific “best X in Y” queries.
Log file analysis! Check how the AI crawlers are going through your website. You will uncover some interesting things and behaviours.
For AI Shopping: Product feed optimization, and make sure you have high-quality images.
For AI Shopping: Product feed optimization, and make sure you have high-quality images.
My #1 tip: Reverse engineer sources. Is your competitor always getting mentioned above you? Check what sources are being used to infer why ChatGPT or Perplexity is favouring them.
When I do this, I often find a particularly favourable comment on Reddit - perhaps in an open thread where you can still suggest your own product as an option.
But I've also found things like a self-published press release is being picked up because it's announcing a particular audience or category, like "We've just launched X product for Y audience". If you also do the same, creating more hyper-specific source material on your blog, press page or help centre, it may be enough to coach LLMs about you and improve your visibility, too.
When I do this, I often find a particularly favourable comment on Reddit - perhaps in an open thread where you can still suggest your own product as an option.
But I've also found things like a self-published press release is being picked up because it's announcing a particular audience or category, like "We've just launched X product for Y audience". If you also do the same, creating more hyper-specific source material on your blog, press page or help centre, it may be enough to coach LLMs about you and improve your visibility, too.
Entity SEO Audits. A thorough review of how your company/brand(s) are represented across the web with a focus on the Knowledge Panel, Wikipedia, Wikidata, relevant review sites etc…
As an enterprise company that just went through a brand refresh, we’re focusing on updating old logos by using the Wikimedia commons upload wizard, reaching out to third parties to ask for logo updates, claiming our knowledge panels, adding organization schema to homepages etc…. A full understanding of any gaps in your entity presence to avoid mixed signals.
We’re focused here because the majority of our AI referral traffic comes from ChatGPT, and our conversion rates are highest here.
As an enterprise company that just went through a brand refresh, we’re focusing on updating old logos by using the Wikimedia commons upload wizard, reaching out to third parties to ask for logo updates, claiming our knowledge panels, adding organization schema to homepages etc…. A full understanding of any gaps in your entity presence to avoid mixed signals.
We’re focused here because the majority of our AI referral traffic comes from ChatGPT, and our conversion rates are highest here.
Publish content with first hand experience whenever possible. LLMs can generate generic content on their own, what they cannot do is generate first hand knowledge of a situation or event. Include phrases like “I tested,” “We used this tool,” or “Here’s what happened after 30 days of xyz.” This gives the model more reason to pull your content into the answer.
Keep the dependency tree short. This means shorter sentences with each sentence giving an answer to one question only. The question doesn't have to be in the content as it's implied by the sentence (answer). AI search will thrive on clarity for ease of giving answers to the searchers and you want to make it easy for LLM's to generate passages with your info included.
Frequently Asked Questions When Learning AI Search Optimization
What's the difference between optimizing for traditional search engines vs AI Search?
The fundamental difference between optimizing for traditional search engines and AI search (like Google AI Overviews, Bing Copilot, Perplexity, etc.) lies in the output format and retrieval model. In AI search, the goal shifts from ranking well to becoming citable, semantically clear, and contextually trusted. You’re no longer just fighting for position — you’re fighting for inclusion.
In traditional search engines, your goal is to appear in top-ranking positions in search results, from which users choose what to click. To improve your content rankings in search results, you optimize your site pages content relevance, link popularity, along with many other signals taken into account to increase clicks that translate to traffic and ultimately, conversions from users searching for relevant products or services.
Instead, AI search engines provide a direct answer synthesized from multiple sources – sometimes without the user clicking anything. In this case, your goal is to be cited in or contribute to the AI-generated response, with possible inline mentions or linked attribution (but often not a ranked link). LLM-powered systems synthesize information by issuing multiple subqueries (query fan-out) and extract relevant content spans from multiple documents. In this case, optimization efforts shift to structured content for easy chunking, entity optimization, citation-worthiness, etc.
How should I structure my content to be more discoverable by AI search engines?
To structure your content for maximum discoverability by AI search engines (Google AI Overviews, Bing Copilot, and Perplexity), you need to optimize not just for indexing, but for chunk-level retrieval, answer synthesis, and citation-worthiness.
- Optimize for Chunk-Level Retrieval: Keep each passage tightly focused on a single concept (One idea per section), keep passages semantically tight and self-contained, each chunk should be independently understandable (rather than needing the whole page for context).
- Optimize for Answer Synthesis: your content must be easy to extract and logically structured to fit into a multi-source answer, start answers with a direct, concise sentence, use natural language Q&A format, use plain, factual, non-promotional tone, summarize complex ideas clearly, then expand.
- Optimize for Citation-Worthiness: AI engines will cite content when it’s perceived as factually accurate, well-structured, and authoritative. To earn attribution, your content must meet higher trust and clarity. Write neutral, fact-based statements, include source citations (link to studies, stats, or experts), show authorship and credentials (EEAT signals), use specific, verifiable claims.
Besides the above, organize your content into topical clusters, use structured data and regularly update your content to keep it up-to-date.
What role do citations and source credibility play in AI search optimization?
Citations and source credibility play a central role in determining whether your content is included in an AI-generated answer and whether your brand or page is explicitly attributed: citations help determine what gets shown. AI search engines synthesize answers by pulling from multiple credible sources. They use citations to attribute facts or statements, provide user trust signals, allow users to verify or explore more deeply. Without perceived credibility, your content might inform the answer, but not be cited.
To be cited (not just used), you need to make it easy for the AI system to trust and attribute your content: Add expert bylines, cite primary sources or studies, write in clear, factual language, use original data or unique insights, use structured data.
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