Search engine optimization, or SEO, has shaped the way we organize and find information on the web for decades. When it comes to generative AI, large language models (LLMs) like ChatGPT, Bard, and Claude are taking center stage.
They produce humanlike text, gather knowledge from many web sources, and serve a growing user base that expects immediate, clear answers.
A pressing question arises: Does the classic Google SEO—our reliable method for ranking in search—affect how LLMs produce and prioritize mentions, citations, and suggestions?
In this piece, I’ll share what’s known, include some real-world data, and show how the connection between SEO practices and LLM results seems to be developing.
Search is Changing
Not long ago, “search” was nearly synonymous with “Google.” Most content creation and optimization revolved around Google’s continuously updating algorithm. Today, however, the field of search no longer operates on a single track.
Recent AI-based search engines and chatbots show that Google remains a leading force, but it is no longer without competition.
Tools like ChatGPT, Perplexity, and Bing Chat have appeared as useful assistants—and sometimes even as the primary option—for countless queries. People now search in many different ways.
But does this reality make our efforts in Google SEO irrelevant?
No. It is a matter of working together instead of competing.
How LLMs Pull Information
Training Data Sources
LLMs are trained on huge data collections. That data is typically a mix of random web crawls, specific websites, or even carefully selected documents.
Some LLMs use nearly up-to-date data for references, such as through web plug-ins or integrated search APIs. Others rely on information limited to a certain cutoff date.
Either way, since many LLM training processes mention “web data” as a main source, the question becomes: “What web data do these LLMs get?”
Relevance Beyond Keywords
Instead of merely counting “keyword frequency,” advanced models measure context, credibility, repetition across sources, and consistent mentions.
If your brand or content is recognized widely and appears consistently across the web, you may receive more favorable treatment from LLMs.
Simply put, the idea of “keywords in titles” from earlier SEO is not enough for LLMs. Instead, the focus is on a steady brand presence, recognized reliability, and repeated mentions across various websites.
Echoing Shared Knowledge
When LLMs generate an answer, they often summarize the common information gathered from multiple sources. If your content is acknowledged broadly, it might be referenced. If your site is hardly mentioned, your brand or sources tend to fade into the background.
Where Google SEO Intersects LLM Results
Some small-scale tests combining LLM behavior and SEO influence suggest that a strong domain rating (like DR from Ahrefs) is tied to whether an LLM mentions your site.
An internal review of more than 400 keywords from an SEO firm found a 77% correlation between top Google ranks and ChatGPT references.
Real-World Case: Why SEO Signals Do Matter
Consider a scenario that shows how SEO best practices might still be important:
- You run a specialized e-commerce site, for example, high-end mountain bikes.
- You optimize for Google SEO by providing detailed product descriptions, strong user engagement signals, and robust domain authority (through digital PR, link building, and more).
- A user asks ChatGPT, “Where can I get a quality mountain bike for advanced trails?”
The LLM scans its reservoir of information. It notices your brand’s repeated appearances across review sites and reputable media sources that refer back to your domain.
Since the user’s query nudges the model to list known brands, and your brand appears widely, consistently, and with reliability, the model includes your brand in its response.
This situation is unlikely if your site has little presence or is pushed aside by low-quality signals that weaken its credibility.
It appears that maintaining strong authority in the traditional Google sense carries over to results from LLMs.
Challenges in Pinpointing “LLM Ranking Signals”
No Single Algorithm
Unlike Google, LLMs are not governed by one hidden formula. Each model (ChatGPT, Bard, Gemini, and others) uses its own set of training data, weightings, real-time search capabilities or not, and other factors. As a result, the signals that affect LLM outputs remain vague and subject to change.
Hallucinations and Knowledge Gaps
LLMs sometimes produce errors or information that isn’t fully accurate.
Also, some models lack access to up-to-date web data. This means that even with good optimization, delays or missing details can lead to fewer mentions in LLM responses.
Inconsistent Citations
Since LLMs are not required to cite their sources, even if your brand or site contributes significant information, the model might offer a summary without giving direct credit.
Ten Tips for Influencing LLM Mentions
1. Maintain strong domain authority
Keep building relevant, high-quality backlinks.
A well-established domain is more likely to get referenced in AI chat responses.
2. Spread consistent brand data
Make sure brand or product details remain consistent across the web.
LLMs prefer repeated mentions from multiple sources.
3. Create thorough, in-depth content
LLMs favor topics and sites that cover subjects in detail rather than brief, shallow posts.
Detailed blog posts, guides, and Q&A pages make a difference.
4. Use structured data
Even if LLM crawlers ignore some schema markup, having a clear structure does no harm.
Google still drives a lot of traffic, so continue to use schema.
5. Use digital PR
Appear in respected and relevant outlets so that LLMs pick up reliable signals about your brand.
Connections with well-known sites encourage repeated references.
6. Keep content fresh
LLMs tend to refer to content that shows current and updated signals over time.
For example, if you released a new product last year, update your product pages with current information from 2024 and include recent user feedback.
7. Build or sustain editorial presence
Work toward earning references from sources like Wikipedia or other high-authority domains.
Many LLMs consider a presence on Wikipedia as a mark of credibility.
8. Consider chat-driven SEO
Since many people now ask AI chat for direct brand comparisons, provide clear differences between your brand and others on various platforms and discussion forums.
9. Optimize for entity completeness
For local or brand-related searches, ensure that all structured details, brand narratives, and information on aggregator sites (such as Yelp, LinkedIn, or Capterra) are complete and accurate.
10. Monitor AI references
Every so often, ask ChatGPT or Bard how your brand is portrayed.
If the information is wrong or missing, check and correct inconsistencies.
Future Outlook
LLMs are still in an early phase of adoption, with improvements in real-time integration and prompt sophistication occurring monthly.
Google SEO’s core principles, like delivering useful, user-centered content and building domain authority, remain in force. They adjust alongside advances in AI.
Some expect that search based on LLMs might gradually reduce reliance on traditional Google search. Others see them as tools that work together.
In the short term, strong SEO signals (such as backlinks, consistent brand mentions, and user engagement) continue to be important for recognition by both conventional search engines and new AI chat platforms.
Frequently Asked Questions
What if my content is missing from LLM answers?
Check whether your brand appears consistently among the top 10-20 web references on the topic. If not, consider broadening your presence with PR efforts, quality backlinks, or encouraging user reviews.
Does real-time SEO matter if LLM data is outdated?
Yes. Some models now use real-time search plug-ins or scan a wide range of aggregator sites. Keeping your metadata current and precise still matters for successful integration with these tools.
Should I rewrite all content specifically for LLMs?
No. Start by refining your top product or service pages to cover topics in detail and ensure your brand information is consistent. Then, observe whether LLMs reference your content in typical user queries and adjust if necessary.
Does overly emotive or marketing language hurt LLM references?
Too much filler or inconsistent brand claims can reduce clarity. LLMs tend to work best with clear, factual, and well-organized text.
For additional perspective, see Impact of Generative AI on SEO: An uncomfortable truth.
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