Last updated: April 2, 2025
Optimizing for SEO was synonymous with optimizing for “Google.” To be honest, for the most part, it still is. Now that “AI Search,” or “Generative Engine Optimization (GEO),” or LLMs are in the picture, how has this transformed the landscape of search?
For one, “zero-click” searches are furthering the gap in SEO because Google is answering more questions within search and reducing the need to visit a website for the answer. Another is producing better content, faster analysis, or automating SEO tasks, which is something companies are looking for their SEO leaders to know.
For me, one that I’m still learning and researching is the various LLMs and how to optimize for them, since the way people search for answers is starting to diversify with ChatGPT, Claude, and others. At the end of the day, my perspective is that as an SEO, my job is to help companies get found. Let’s dig in further to learn these three types of AI search and how to optimize them!
TL;DR: How AI Search Is Changing SEO
AI Search is reshaping the SEO landscape, beyond just optimizing for Google. There are three main types of AI Search models:
1. Training-Led Models (e.g., Claude, Llama)
→ Focus on evergreen, authoritative content, structured data, and brand mentions to ensure your site is part of their training data.
2. Search-Led Models (e.g., Google AI Overviews, Perplexity)
→ Prioritize freshness, featured snippets, crawlability, and E-E-A-T signals for visibility in real-time AI search results.
3. Hybrid-Led Models (e.g., ChatGPT with browsing, Gemini)
→ Combine both strategies—keep content up to date, target FAQs, and improve brand presence to be referenced in AI-generated answers.
Bottom Line:
Adapt your SEO by understanding these AI models, creating timeless and fresh content, and focusing on brand authority to future-proof your visibility. If we don’t adapt, we risk becoming invisible to AI-driven discovery systems.
Three Main Categories of AI Search
1. Training-Led AI Models (Pretrained AI Models)
💡 Definition: AI models trained on vast datasets, but do not actively retrieve new data from the web when responding. Training-led AI models will take a “frozen snapshot” until the next update happens.
💡 Examples:
- Mistral (open-weight model for enterprises)
- Llama (Meta’s large language model)
- Claude (Anthropic’s AI assistant)
- GPT-4 Turbo (when browsing is disabled)
How They Work
- Models train on datasets from books, research papers, websites, and other structured knowledge sources.
- They generate responses based on their pre-existing training, meaning they have a knowledge cutoff date.
Ways to Optimize for this Training Model
✔ 1. Create Evergreen, Authority Content
Training-led models yearn for high-quality, well-cited sources in their training data. As a result, I’d focus on long-form content that remains timeless and authoritative. When developing your content, consider whether it’s set up as well-structured information with citations. Unsurprisingly, AI models and even Google gravitate to this style.
✔ 2. Prioritize Structured Data
Talk schema to me, baby! SEOs know that structured data (FAQ, HowTo, Article) isn’t about rich results anymore—it’s about improving search visibility and helping AI models extract meaning effortlessly. I know ensuring you implement semantic HTML (<header>, <nav>, <img>, etc.) helps as it makes it easy for models to extract meaning.
I am a big fan of schema because I’ve seen firsthand, when I implemented this for e-commerce sites, that I saw up to 30% more impressions and 20% more clicks just for product pages and product listing pages. It’s low-hanging fruit for our e-commerce SEOs!
✔ 3. Build Topical Authority & Earn Mentions
We know AI models learn from reputable sources—getting cited in research papers, trusted websites, or government pages will help you a ton. How do you get there? Buying links is so not fetch. Instead, conduct your own original research, case studies, and data analysis that get referenced. Wil Reynolds has championed this for years, producing valuable content that people want to go back to. In the sea of sameness how are you going to stand out?
✔ 4. Leverage AI Content Summarization
Since AI summarizes and reuses learned content, write clear key takeaways and TL;DRs in your articles. I know I love a good TL;DR if I’m short on time, and it seems AI has picked up on that as well.
Bonus: Content optimized for training-led models also has a higher chance of being referenced by AI models like ChatGPT and Claude in future updates. Great to emphasize long-term value to stakeholders.
📣 Tips on Educating Your Stakeholders
✅ Position It as Future-Proofing the Brand:
Aiming to improve visibility in training-led AI models is an excellent brand-building exercise. Consider the fact that the use of AI tools is increasing, and the data in training-led models is updated once a year or longer. As a result, potential and current customers’ perception of your brand is shaped and influenced. Creating authoritative content ensures the brand is represented accurately. Furthermore, you could also point out that any misinformation or lack of representation in these models can erode trust and impact customer decisions.
✅ Propose Content Alignment Audits:
Start with a content alignment audit to let the data do the talking. One good place to start if you don’t have a budget is using Crystal Carter’s Google Sheet on tracking Brand Visibility in LLMs (requires name & email, but it’s worth it). I found her article on this to be a great starting point. From here, you can tweak it to assess if the core product and brand information align with how AI models present the company in responses.
📌 Key Takeaway:
To optimize for training-led AI models, focus on timeless, research-backed, authoritative content that AI models are likely to train on.
Now that we’ve covered how to optimize for training-led models, let’s dive into search-led AI models and explore how to stay ahead in the volatile world of AI-powered search.
2. Search-Led AI Models (Real-Time AI Search)
💡 Definition: AI models that retrieve live web search results to generate responses.
💡 Examples:
- Google AI Overviews (SGE – Search Generative Experience)
- Perplexity AI (cites sources directly)
- You.com (real-time search with AI)
- Kagi (privacy-focused AI search engine)
How They Work
- These models fetch and analyze live search results before responding.
- Some cite sources and link back, while others summarize web content.
Ways to Optimize for this Training Model
✔ 1. Optimize for AI Overviews & Featured Snippets
SEO’s are familiar with using structured headers (H1, H2, H3). Ensure you have concise answers for featured snippet eligibility. Answer questions directly within the first 2-3 sentences. Format answers in bullets, tables, or short paragraphs.
✔ 2. Stay Fresh & Update Content Regularly
Staying fresh wasn’t just for the Fresh Prince; AI search engines prioritize recent content over outdated articles. Regularly update key pages with new data, insights, and citations. Use Google’s lastmod attribute in XML sitemaps to signal updates.
✔ 3. Optimize for AI-Cited Sources
This is similar to building authority and trust with some of the suggestions listed above. However, some AI search tools (like Perplexity AI) link back to sources. I don’t mean a few here and there, but A LOT (see image screenshot below). Go back to the old age saying of improving E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) factors to your content so it has higher chances to be cited.
✔ 4. Improve Crawlability & Load Speed
Since AI models pull from live results, ensure your pages load fast (under 2.5s). Fix broken links, orphan pages, and indexing issues to improve discoverability. These are all traditional technical SEO activities that you are already doing or working towards, so this is great for leadership to see that your efforts will go beyond our web-based search engines (Google, Bing, etc)
📣 Tips on Educating Your Stakeholders
✅ Frame It as Risk Mitigation:
Your stakeholders should know that AI-augmented search is already impacting CTRs, and failing to optimize could lead to lower visibility in AI-driven SERPs. Showing real examples of competitors in this space is a great way to persuade and motivate action. Focus on where they’re capturing featured snippets that dominate AI responses.
✅ Show Data on Click Shift Trends:
Highlight studies or reports showing that AI-generated snippets are diverting clicks from traditional organic links. Present a before/after CTR analysis from beta programs like SGE to emphasize the importance of owning featured positions.
✅ Use Business Impact Language:
Tie optimization efforts directly to bottom-line goals: “If we don’t adapt to AI search visibility, we risk losing X% of organic traffic, which translates to Y dollars in revenue.” You can use a simple formula such as the following:
Avg. CTR of snippet terms x search volume = potential traffic
Potential traffic x RPV (revenue per visit) = potential revenue
✅ Run a Pilot Program:
If there’s one thing I encourage my team members to do or incorporate into my overall SEO strategy, it’s experimenting and testing. You can suggest running a low-risk experiment (e.g., schema improvements + content updates) on high-value pages to demonstrate the potential uplift in AI-driven visibility. Measure impact over 3–6 months and present findings. Either way, it’s a win-win in terms of where this sits as a priority. Don’t push things too hard and go from a fear angle; instead, you want to test a hypothesis to understand if this opportunity is worth going after for the business.
📌 Key Takeaway:
To rank in search-led AI models, optimize for featured snippets, freshness, and structured data while maintaining fast-loading, crawlable pages.
Search-led models bring speed and recency — but what happens when you mix that with the deep knowledge of a trained model? That’s where hybrid-led models come in. Let’s take a closer look at how this model works!
3. Hybrid-Led AI Models (Mix of Pretraining & Search Retrieval)
💡 Definition: AI models that use pre-trained knowledge but also retrieve live search data when necessary.
💡 Examples:
- ChatGPT (when browsing is enabled)
- Google Gemini
- Microsoft Copilot (Bing AI)
- Poe by Quora (multi-model AI assistant)
How They Work
- It defaults to pretrained knowledge but can browse and fetch web results when asked.
- May or may not cite sources depending on the query.
Ways to Optimize for this Training Model
✔ 1. Optimize for Both Evergreen & Real-Time Search
It would not be surprising that you’d combine optimizations from the other two models. Specifically, you balance long-form, timeless content (for training) with regular updates (for live retrieval). Target high-traffic, evergreen keywords that AI models reference often.
✔ 2. Target FAQ & Conversational Queries
The good thing with optimizing for LLMs is that there is a lot of overlap with traditional SEO techniques. These AI models often respond to “how-to” and “why” questions. I recommend adding FAQ sections to blog posts and using structured data (FAQ Schema).
✔ 3. Focus on Branded Search & Digital PR
AI chatbots may paraphrase content instead of directly linking. Improve brand awareness so that AI-generated answers mention your site. Use PR services like Qwoted or guest posting to increase citations.
✔ 4. Monitor AI-Generated Mentions
Check if AI models reference your content (tools like Perplexity may link back) or utilize Crystal Carter’s Google sheet to utilize prompts manually and note which ones call your brand out. If you have ChatGPT, you can also use Aleyda Solis’ custom brand visibility checker GPT here. Another approach is using Google Alerts & Brand Monitoring Tools to track brand mentions in AI responses.
📣 Tips on Educating Your Stakeholders
✅ Sell It as a Personalization Play:
Frame hybrid optimization as aligning with broader personalization trends. Emphasize that these models adapt results based on user signals, meaning brands that meet multiple intent types will dominate dynamic SERPs. The data you have on brand visibility, as well as where your competitors are, will help both you and leadership decide the priority of the opportunity.
✅ Make It a Customer Retention Play & Propose Creating Multi-Intent Content Hubs:
The general rule of thumb is that acquiring a new customer is much more costly than retaining one. You can explain that personalized experiences often drive customer retention and loyalty. A classic content strategy that is tried and true is developing content hubs that target multiple stages of the funnel. This will enhance the likelihood that hybrid models surface relevant results based on evolving user needs. Keeping it fresh is a challenge. I recommend dynamic content frameworks that update automatically based on real-time behavior. Some examples are customizing page content based on visitor profiles or adding dynamic product recommendations carousels.
📌 Key Takeaway:
To rank in hybrid-led AI models, optimize for both static content & real-time search, target conversational keywords, and improve brand visibility for AI-generated responses.
Hybrid-led models give us a glimpse into where AI search might be headed — a blend of memory and immediacy. So, what does this all mean for your SEO strategy today? Let’s bring it all together.
Conclusion: How to Future-Proof Your SEO for AI Search
- AI search is changing SEO strategies and requiring adaptation.
- Understanding the three types of AI models—Training-led, search-led, and hybrid-led—helps businesses create content optimized for AI discovery.
Key takeaways:
- For Training-led models → Focus on timeless, research-backed authority content.
- For Search-led models → Optimize for real-time search visibility, freshness, and featured snippets.
- For Hybrid models → Balance evergreen and real-time content while improving brand mentions.
- Stay ahead by monitoring AI-generated search trends and adjusting SEO strategies accordingly.
Sources:
- 📚 Seer Interactive: Once I read this article, it inspired me to go deeper into this idea. Would highly recommend checking this out: https://www.seerinteractive.com/insights/theres-3-types-of-ai-search-do-you-know-which-are-you-optimizing
- 🕵️ Wix SEO Resource: Already referenced this one a lot in this article, but found this useful to understand how to audit your website to see if AI Search is worth looking into now. https://www.wix.com/seo/learn/resource/track-llm-brand-visibility
- 🧠 GEO Academic Paper: This is the first published academic paper on GEO or Generative Engine Optimization. It gives you a lot of insight https://arxiv.org/pdf/2311.09735