Before the widespread implementation of AI, the goal of search engine algorithms was to produce a ranked page of links whose content was relevant to the keywords found in the user’s query. Modern search engines like Google, Bing, and Yandex have shifted to providing AI-generated summaries that either answer the user’s query or provide the most relevant information.
Because of AI-generated summaries, many users turn to zero-click searches, as they no longer need to click through results page links. In this article, we discuss how AI-powered search engines select what content appears in their results and how to adapt both classic SEO concepts and new GEO tactics to optimize content for AI search.
How AI Has Changed Modern Search
To better conceptualize how search engines choose what content appears in their answers, you first need to understand how AI has shifted the way that modern search engines function. The main focus of classic SEO strategies was to optimize content through keyword placement, internal linking, technical SEO, and related tactics to get a site’s webpages to rank highly on search engine results pages (SERPs).
The main objective of generative engine optimization (GEO) is to optimize content and data so they can be mentioned and cited in AI-generated summaries. Although your site’s content still needs to be understandable to your human audience, it also has to be highly digestible and easily summarized by large language models (LLMs) that power AI-powered search engines.
What Is Retrieval-Augmented Generation?
Retrieval-Augmented Generation (RAG) is the core mechanism behind AI search engines. This process starts with the traditional setup: a user submits their query, and the program retrieves relevant and authoritative source documents via a standard search index.
What makes RAG so unique is that it creates a prompt that combines the user’s original query with relevant indexed sources and instructs a large language model (LLM) to generate a synthesized answer that fully covers the topic. The final step of the process is the mechanism that provides citations linking back to supporting sources, so the user knows where each unique piece of information originated.
Adapting to AI-Friendly SEO Tactics
Because modern search engines now work so differently, businesses have to evolve their SEO strategies to stay relevant. Thankfully, some SEO fundamentals that worked for past search algorithms remain important for GEO, including keyword placement, backlinking, technical SEO, and mobile optimization.
There are, however, distinct ways to optimize these aspects to help content be seen and chosen by AI search. Effectively combining traditional SEO with new GEO concepts is the best way to rework your content and create new content that is likely to appear in AI-generated answers.
Traditional SEO Tactics that Still Matter
Some AI visibility beginners might think that classic search engine optimization tactics no longer matter, but there are several that help get your webpages included in the traditional index that AI search engines give to their large language models before generating an answer to the user’s query.
Listed below are some traditional SEO tactics that still help improve AI visibility:
- Internal Linking
- Backlinking
- Keyword placement
- Indexability
- Technical SEO
- On-page SEO
- Mobile optimization
Internal Linking
Internal linking helps organize your site’s content so that AI systems can crawl it more efficiently. The bots and LLMs do not view each webpage in isolation but rather as part of a comprehensive whole, and a well-organized website should have major landing pages linked to supporting cluster pages.
Internal links help the AI systems behind modern search engines contextualize how different topics and subtopics are related to one another. AI search engines aim to provide users with the most comprehensive answers to their queries, so a deep, contextual understanding of your site increases the likelihood that it will be mentioned or cited.
Backlinking
Getting other sites to link back to your site’s webpages used to help traditional search algorithms decide how high a piece of content might rank in SERPs, but the indirect value of backlinking is now to increase domain authority.
Websites linking to your webpages signal to AI search engines that your brand is authoritative and trustworthy, and these backlinks are even more valuable if they come from larger, more reputable websites relevant to your industry. Backlinks tend to result from comprehensive, accurate content; they help drive referral traffic and can help form relationships with other brands.
Keyword Placement
Before they give the user’s query and relevant indexed sources to the large language model, search engines first have to go through the basic process of identifying the most relevant keywords in the user query and finding content that utilizes those keywords. It is also important to ensure that keywords are naturally incorporated into the content (rather than keyword stuffing).
Any business that wants its content to appear in AI answers should focus first on the most important keywords in its industry. Long-tail keywords are a helpful focus here because users often type longer, more complex phrases into search engines, and those need to be included as well. Businesses can even hire SEO agencies to identify long-tail keywords and other factors to refine their current content strategy.
Indexability
Your site’s indexability remains paramount to AI visibility because LLMs draw from and cite indexed sources in their synthesized answers. AI search engines use bots to crawl the internet for the sources they use to generate answers, so you need content that can be reached, rendered, and stored.
Brands that want to ensure their site is indexable need to ensure that major AI systems (e.g., Google, ChatGPT, Yandex AI) are not blocked in their robots.txt files. This can happen accidentally, so double-check that your site’s content can be reached. Businesses also need to fix technical issues such as broken internal links, redirect loops, or duplicate content.
Technical SEO
Improving your site’s technical SEO helps create the conditions for AI systems to retrieve information and interpret it accurately. The bots that AI-powered search engines use must quickly crawl your webpages to gather the information they need for AI-generated summaries.
Technical SEO starts with fixing any loading issues or errors and removing content clutter or messy coding to maintain high crawlability and speed. Every website should also have a clean infrastructure with machine-readable data formats, AI bot permissions, entity disambiguation, etc.
On-Page SEO
On-page SEO is about optimizing individual webpages so that they are easily read and understood by both humans and machines. The objective here is to have content with clear meaning, simple, clean formatting, and consistent context, rather than endless paragraphs stuffed with target keywords.
To improve each webpage’s on-page SEO, ensure all content is well-structured with descriptive headings. Use title tags, headers, subheaders, meta descriptions, and other logical hierarchies to help a page both rank in indexed search and be cited by popular AI systems.
Mobile Optimization
Major AI search engines, especially Google, tend to use mobile-first indexing when they are crawling the web. This means that the mobile version of your site is the primary version that is both crawled and indexed.
The basics of mobile optimization start with avoiding images that affect loading times, using large fonts, and avoiding buttons that are hard to click. Having webpages fully optimized for mobile devices also makes your content more legible to AI systems and serves as an indirect signal of authority.
New GEO Concepts to Utilize
Once you’ve gotten all of the SEO basics down, it is time to incorporate some new concepts based on generative engine optimization (GEO) so that you can make sure your site is completely AI-friendly.
Listed below are new GEO concepts that can majorly help improve your site’s AI visibility:
- Intent
- Citation
- Structure
- Schema markup
- Authority and trustworthiness
- Content freshness
Intent
One of the most unique things about AI-powered search engines is that they look for the intent behind a user’s query rather than just focusing on keywords. These LLMs are trained to focus on meaning, context, and how different topics connect so that a user’s question can be fully understood and then answered.
For example, Google’s AI Mode uses a query fan-out method, breaking the user’s query into several subtopics and running multiple queries at once to achieve a deeper understanding than a traditional search engine would. As a result, these new AI systems are looking for content that comprehensively covers and understands a range of topics and subtopics.
Citation
AI search engines also tend to include content that can expertly cite the sources behind its information. They especially prefer specific and verifiable data over more vague statements, meaning. For example, a webpage dedicated to the rising literacy rates among young adults should include exact percentages over periods of time, using reputable sources.
Using citations naturally makes AI systems consider your content more authoritative and thus more citation-worthy. Try to add sources whenever mentioning specific data or facts, add statistics wherever applicable, and include expert quotations. These new models want content that is dense with specific information in addition to having the right keywords and quality backlinks.
Structure
How content is structured helps AI search engines decide whether or not to include it in their summarizing answers. The visual organization of each webpage must be clean and consistent, with direct questions paired with clear answers, so that AI systems can use them directly in answers to similar user queries.
AI systems use your on-page HTML formatting to understand your content’s structure, so you can help guide them by focusing on headings, bulleted lists, and other essential structural elements. Include relevant FAQs at the bottom of webpages, especially landing pages, and break content into smaller, focused sections rather than cramming it into a single long paragraph.
Semantic Chunking
Semantic chunking is the GEO-focused process of turning content into information that has standalone clarity. Every definition, statistic, or concept needs to be presented in a form that AI systems can extract without much surrounding context. To put it another way, make it easy for these models to lift facts and figures from your webpage without having to read the rest of it.
Definition-First Content
Part of your site’s organizational structure should emphasize definition-first content. Webpages that open with a clear, direct definition of the topic currently being discussed are much more likely to be cited by AI-powered search engines. Focus less on using traditional narrative-style formatting with keyword placement and instead get straight to the most relevant answer to a user’s related queries before expanding further.
Schema Markup
Schema markup is an incredibly technical GEO concept that refers to how data is structured on your site. A schema is a type of code that helps AI-powered search engines understand your content by providing unique labels that identify plain text as reviews, products, FAQs, etc.
FAQPage, HowTo, and QAPage are the most popular examples of GEO-focused schema markup because they structure your content as direct answers to unique questions. If these AI systems can easily contextualize the information on each of your webpages, they easily convert said information into quick answers to user queries.
Authority and Trustworthiness
AI search engines also tend to only include content from authoritative and trustworthy sources. For example, Google has explicitly mentioned that its AI systems use a framework called E-E-A-T, which stands for Experience, Expertise, Authoritativeness, and Trustworthiness. Sites that want to end up in a Google AI Overview need high-quality content that signals these specific characteristics.
To improve your site’s brand authority, prioritize accuracy by using facts and citing high-quality sources whenever you provide specific data and statistics. Quality backlinking also signals your authority, as does forming strategic partnerships with more established names in your specific industries.
Content Freshness
Content that has been recently published or updated is also more likely to be cited by AI search engines. The bots and LLMs behind these new systems tend to index and include content published or updated within 3 months of the user’s query submission date.
To maintain content freshness, keep a regular publishing schedule and post unique, relevant content. Keep tabs on emerging trends within your industry and update your content frequently. To help AI search engines determine that your content is fresh, display “last updated” dates at the top of each webpage.
AI Visibility Success Metrics
Since the implementation of AI has changed how content appears in search engine results, sites need to adopt new success metrics. Some standard SEO-related metrics like clicks, impressions, and search volume are still important to track, but they don’t give the entire picture of how your site’s content is being included in AI search.
Mentions refer to how often your specific brand is mentioned in AI-generated answers, and citations refer to how often your specific brand is authoritatively cited in those same answers. To deepen the context, brand sentiment helps explain how those brand mentions were perceived, and competitive share of voice reflects your site’s percentage of mentions and citations relative to competitors.
Share of Model
Share of model is a new AI visibility metric that measures how often your brand is mentioned or cited across AI platforms like ChatGPT, Gemini, or Claude. To properly track the share of the model, you have to determine how often your brand appears and is cited in AI-generated answers while also tracking citation sentiment, share of voice across different AI platforms, and AI-referred traffic (through GA4 attribution).
Key Takeaways
Modern search engines function very differently from how they did before the implementation of AI. If your site wants its content to be selected as answers by AI search engines like Google or Yandex, your content strategy needs to be updated accordingly. Certain traditional SEO tactics, such as quality backlinking and mobile optimization, remain useful for getting webpages indexed by AI systems.
Businesses also need to incorporate new GEO-focused concepts, including query intent, schema markup, brand authority, and content freshness. Sites that achieve both SEO and GEO success are more likely to have their content included in AI-generated answers, and these instances can be measured using new metrics such as brand sentiment and competitive share of voice.
