AI-powered search is shaking up everything. I see friends who used to rely on Google now simply ask ChatGPT for the best approach to pitch a big client, or they open Bing Chat to find the perfect laptop for video editing.
I’ve been involved with AI search for a while, and understanding how large language models (LLMs) operate—and how they differ from more traditional, search-engine-like AI—is a useful skill.
In this article, I break down the different types of AI searches, explain how LLMs gather data, and share 15 of my tips to help you optimize your content so it gets included in future AI answers.
Different Types of AI Search
There isn’t just one type of AI search. At least three general categories have started to appear:
Training-First Systems
Think of Claude (by Anthropic) and Llama (by Meta). They rely on huge training data that remains fixed until the next model update. The next time you ask them about a current product, you might get outdated info if they haven’t been updated.
Upside: They can be very creative because they rely on an enormous (yet fixed) knowledge base.
Downside: They might not have the latest information.
Search-First Systems
These systems—like Google’s AI Overviews, Bing’s AI Summaries (in Bing Chat), or specialized AI summaries from search engines (for example, Perplexity)—depend heavily on real-time indexing, much like a search engine works.
Upside: They are current, so you can optimize content and see results almost immediately.
Downside: They sometimes reference more widely known or established sources, so breaking through as a lesser-known brand can be a challenge.
Hybrid Systems
ChatGPT, for example, can work like a training-first system but, with browsing enabled, it can also include up-to-date data from Bing. Google’s upcoming Gemini, a rumored hybrid system, will combine real-time queries with a sophisticated training dataset.
Upside: They decide when to rely on older training information or fetch brand-new data.
Downside: This setup can be unpredictable. Sometimes you get current answers, and other times you get older information.
ChatGPT vs. Google’s AI Overviews
ChatGPT often depends on a fixed dataset. Even with browsing turned on, it simply pulls the top few results from the connected search engine, using older text-generation logic.
It is a conversation-based system that values clarity and context, but it does not always incorporate all the HTML signals from your site.
By contrast, Google’s AI Overviews are built into mainstream Google search.
If Google believes a query can be answered in a quick, generative format, it offers an AI summary at the top of the results.
This feature, part of an experimental phase (SGE or “Search Generative Experience”), pulls live data from the web almost immediately, following Google’s ranking standards.
I have tested this myself. When I update a page on my site, Google’s AI Overviews might show that updated information within a few days, citing me as a source—while ChatGPT may continue to use older data for weeks unless I specifically ask it to browse.
How LLMs Gather and Select the “Right” Data
Large language models like GPT, Claude, or Llama follow these steps:
- Pre-Training
They use vast amounts of text from books, the open web, and curated data sets. - Tokenization and Encodings
The text is broken into tokens. The LLM looks for patterns in how these tokens occur together to predict likely sequences. - Fine-Tuning
Many LLM vendors perform an extra round of training using specialized text to shape the model’s tone or style. - Reinforcement from Human Feedback
They then refine the model’s responses using human-labeled data indicating what is correct and what is not.
In short, these models are pattern matchers. They notice brand mentions in the training data or in the real-time search index.
If your brand is consistently linked to a certain concept (for example, “best security software”), that repeated association can prompt the LLM to mention you when asked about that topic.
There is still some uncertainty. People discuss and guess, but the exact process each LLM or generative search algorithm uses to choose sources is not fully documented.
15 Tips to Optimize Your Content for AI
Below is a detailed list of best practices. Some are common sense from SEO, but they need a little adjustment for LLMs.
1. Emphasize Relevance Over Keyword Count
LLMs are pattern-based. They pick up on context, not just repeated words. Write specifically about “best budget trekking gear for families” rather than repeating “family gear cheap cheap cheap.”
2. Establish Your Brand and Product in Reputable Outlets
When models repeatedly see your brand connected with terms like “expert in data analytics,” they start to make that association. Aim for coverage in respectable media within your industry.
3. Use a Natural, Conversational Tone
Content structured in Q&As, lists, and everyday language is easier for these systems to process.
4. Keep Facts and Explanations Clear
It is important to back up your statements with numbers, studies, or actual data. LLMs pick up on references to real events.
5. Publish on Well-Trusted Websites
Answer questions on sites like Quora or join knowledgeable communities on Reddit. LLM training sets often scan these sites to capture how people talk and which brands are mentioned.
6. Include Structured Data
For real-time AI Overviews (like Google’s), structured data helps. Use product or FAQ schemas to make your content easier for search-first AIs to highlight.
7. Earn Mentions from Trusted Sources
Your website content alone may not be enough. LLMs also consider data from major directories and popular sources. Appear in trusted directories, industry wikis, or large aggregator websites.
8. Build Consistent Associations
If you want your brand to be known for “family-friendly accounting software,” keep featuring that exact phrase in relevant discussions all across the web. Over time, this context consistency can echo in the model’s responses.
9. Write for Specialized Outlets
In smaller niches, there is less noise. Write guest posts on niche sites where your brand may be more noticeable in the training data.
10. Craft Content Ready for AI Formats
Write in short paragraphs and use headings that match how someone might ask a question. For instance, a user might search for “Which CRM is best for midsize nonprofits?” A heading like “CRM Solutions for Midsize Nonprofits” followed by a list of reasons can be very effective.
11. Use Real-Time Tools
For search-first or hybrid systems, ensure your content is current and that your website follows sound SEO practices. If the AI pulls recent blog posts, you want to be well-ranked.
12. Incorporate Q&A Sections
Large language models appreciate information presented in a question-and-answer format. Add a FAQ section to your posts that directly answers likely user queries.
13. Write with a Specific Audience in Mind
Describe hypothetical scenarios that match your brand’s typical user. Repeated mentions in these contexts can help the model remember your brand for that audience.
14. Reference Credible Sources
Mention and link to trustworthy sources. This can improve the likelihood that the AI sees your content as reliable and may help with the citations in AI summaries.
15. Stick to Ethical Practices
Avoid spamming or generating large quantities of low-quality content. These systems are quick to notice unusual patterns and may ignore your brand if things seem off.
LLM vs. AI Overviews
Sometimes people ask which optimization tactics work best for ChatGPT compared to Google’s AI Overviews. Here is a simple table:
Frequently Asked Questions
How soon will my brand appear in LLM answers?
It depends. Training-first LLMs might not show your brand until the model is updated, which could take months. Search-first or hybrid systems might pick up changes within days if you rank well.
Do I need a huge budget to rank in AI outputs?
Not really. In smaller niches, a steady presence through guest blogging and credible mentions on a trustworthy site can be very effective. In larger areas, bigger brands might have an advantage.
Is there a difference between optimizing for ChatGPT, Bing Chat, or Google’s Overviews?
Yes. ChatGPT may use older training data. Bing Chat or Google’s Overviews might pull current information from your website. It is wise to maintain solid SEO practices while also building your brand’s presence.
Do I need to use schema markup?
It is useful for AI Overviews like Google’s. Although it might not be as important for ChatGPT, it still helps with search discoverability and overall SEO.
Can AI mention me incorrectly?
That can happen. AI might sometimes refer to your brand in the wrong context. Keeping your messaging clear and consistent across your site, as well as managing where and how your brand is mentioned, can help reduce errors.
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