What an LLM Is & How It Changes Search: 5 SEO Tips

Large language models (LLMs) have fundamentally changed the way people ask questions and get information online. Users can now get direct answers within AI chat interfaces, bypassing search engines entirely.

This shift is already showing up in marketing budgets. A recent Clutch survey found that the majority of companies — 78% — now invest in generative engine optimization (GEO). It signals how leaders already treat LLM-driven search as a core marketing channel.

Read on to learn more about how LLMs are reshaping search behavior and what you can do to adapt your SEO strategy to show up in LLM-driven search results.

What Is an LLM?

An LLM is an artificial intelligence system trained on massive text datasets to understand patterns and generate human-like responses. Models like ChatGPT, Claude, and Google’s Gemini are a few popular examples.

In AI search engines, the LLM synthesizes information from many sources and directly answers a question. For example, Google describes its AI Overview feature in Google Search as a way to help with more complex questions that previously took multiple queries.

This shift in search now creates a new competition for visibility inside AI-generated answers, instead of just traditional page links in Google, Bing, or Yahoo.

How LLMs Are Changing Search Behavior

Large language models are changing  user search behavior in three key ways:

• Shift in user intent: Since LLMs can handle longer context, people are asking more complex queries as multipart questions. For instance, people ask for in-depth comparisons and recommendations rather than simply typing in a few keywords.
• Less reliance on SERPs: Google has already expanded AI Overviews to over 200 countries. Similarly, ChatGPT claims to have 700 million weekly active users. As users get answers directly through LLMs, the traditional 10 blue links matter less to them.
• Rise of zero-click searches: A recent report shows that in March 2025, 27.2% of U.S. searches ended with zero clicks as compared to 24.4% a year earlier.

These shifts in traditional search behavior demand a change in your SEO approach.

5 SEO Tips for LLM-Driven Search

Adapting to LLM-driven search requires a complete strategic shift in how you approach traditional search marketing. The following five practical tactics will help maintain visibility in AI answer engines as search evolves:

1. Structure content for AI readability
2. Double down as an expert
3. Optimize for GEO & SEO
4. Refresh old content with AI intent in mind
5. Refine KPIs for the AI era

1. Structure Content for AI Readability

LLMs extract and present answers synthesized from multiple sources. To appear in AI answers as one of the cited sources, you need clear sections that directly answer user queries. For example:

• Write content that starts with a tight summary.
• Follow the overview with scannable H2 and H3 sections that answer the why, what, and how.
• Use descriptive headings and short paragraphs that isolate a single idea.
• Create concise FAQs that restate key answers.
• Add comparison tables that resolve “A vs. B” questions.

Also, add structured data where it fits. Use schema types such as Product, Organization, or Service to clarify entities.

While search engines don't promise a specific boost from schema, structured data helps models understand page meaning at scale and can influence which snippets or citations appear. Google’s documentation for site owners confirms that AI features like AI Overviews and AI Mode look for content that answers a query directly and clearly.

These assets give AI a clean snippet to quote and give users who land on your page a quick way to scan for answers.

2. Double Down as an Expert

Both LLMs and SEO rely on signals of expertise and trust. Google’s guidelines continue to emphasize experience, expertise, authoritativeness, and trustworthiness (E-E-A-T). Even though these are not a single numeric score, they frame how search quality raters and even AI systems evaluate helpful content.

One simple way to demonstrate E-E-A-T is to make real expertise obvious across your pages. A few practical ways to do this include:

• Using named authors with expertise credentials.
• Publishing original survey data with a transparent method and sample size.
• Creating step-by-step case study breakdowns with verifiable outcomes.
• Using screenshots of detailed steps or explanations in educational blog posts to reduce ambiguity.

All these expertise signals establish your brand as an authority on any topic or industry and give AI a clear reason to cite you in relevant user queries.

3. Optimize for GEO + SEO

Generative Engine Optimization sits next to traditional SEO, not apart from it. Treat both as a part of "search marketing." Your goal is to make content easily cited by AI while still ranking in traditional organic results. Here are a few practical tactics to optimize for both GEO and SEO:

• Align topics to AI answer formats: As people are asking more detailed questions, create pages that directly address such in-depth how-to questions and comparisons. Use clear structure and add short takeaways that AI can quote. Google’s documentation still focuses on helpful content. That emphasis still applies in AI experiences.
• Optimize where users start their search: Bing’s Copilot Search and Google’s Gemini features sit on many apps across desktop and mobile that millions of users already use. Similarly, more users now use ChatGPT Search and Perplexity as their default search engines. Use tools like Ahrefs to check whether your content is surfacing across AI answer engines and optimize for them.

Marketers are already implementing this combined GEO + SEO approach, which is reflected in their increasing spending on GEO. 63% of marketers in our Clutch survey said that they plan to increase their GEO budget in the next 12 months.

4. Refresh Old Content With AI Intent in Mind

The traditional SEO process worked by creating pages that revolved around a primary target keyword. For a complex answer, a user often had to visit multiple pages, each of which focused on such target keywords.

LLM-driven search changes that model completely. It bundles user queries into one question to provide the single best answer. Thus, page optimization around one target keyword is no longer efficient in an AI-driven search environment. Instead, create in-depth pages that answer all relevant questions users might have about a topic.

Here are simple steps you can take to refresh old content and optimize it for AI-driven search:

• Start by pulling your highest-value URLs and auditing them against real AI answers for target queries.
• If Google’s AI Overview or ChatGPT already provides a multi-step summary, align your page with those sub-steps.
• Add a clean summary on top that addresses the full question.
• Break the body into sections that mirror the steps the LLM lists.
• Cite up-to-date sources for every factual claim.

However, a rise in zero-click searches means traffic to your website might not increase even when AI visibility improves. So, look for indirect benefits of zero-click marketing, such as increased brand mentions and brand authority building. Those are real signals that the optimization effort is working for your target audience, who might read AI summaries and then search for your brand.

5. Rethink KPIs for the AI Era

Traditional SEO key performance indicators (KPIs) lean on user clicks and website visits per session. AI answer engines change that. You now need metrics that capture visibility and influence before a click occurs.

Build a KPI set that pairs AI search intent coverage with downstream impact across your products or services.

• Answer presence rate: Measure how often your pages appear as cited sources under AI Overviews, ChatGPT Search, Perplexity, or Bing Copilot Search for target questions. Also, track pages by marketing funnel stage. Most of these AI search engines include a clickable link to the cited sources within the answers. Your goal is to be one of those links.
• Brand search lift after exposure: To test this benefit, publish or refresh content for a cluster of AI-answered queries, then measure brand query volume or direct visits over the next few weeks. Several studies already show the rise in zero-click searches. That makes branded search a practical way to capture latent demand created by brand mentions across AI-generated answers.
• Sales-assisted influence: Track mentions of your research or pages in sales calls and requests for proposals. If AI answers cite your original data, prospects may repeat those stats back to your team. Tie those mentions to marketing funnel stages.

These KPIs give marketing leaders a view that matches how search works now. You then evaluate coverage in AI answers and connect that exposure to actual demand across your products and services.

Own Visibility in LLM-Driven Search

LLMs now sit at the front of search experiences and synthesize information into quick answers. All AI answer engines reiterate a goal of saving people time by directly presenting a clear response. This change in search behavior explains why GEO budgets have climbed.

However, AI-driven search is still in its early stages of development. Teams that adapt now with GEO + SEO will likely protect and grow their presence across AI answers. Want to accelerate your LLM-driven search strategy in the right direction? Choose the right partner from our vetted list of top generative engine optimization companies.

About the Author

Anna Peck Content Marketing Manager at Clutch

Anna Peck is a content marketing manager at Clutch, where she crafts content on digital marketing, SEO, and public relations. In addition to editing and producing engaging B2B content, she plays a key role in Clutch’s awards program and contributed content efforts. Originally joining Clutch as part of the reviews team, she now focuses on developing SEO-driven content strategies that offer valuable insights to B2B buyers seeking the best service providers.

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