Technical SEO for Generative Search

As search evolves with generative AI, the technical foundations of SEO are more important than ever. A website’s behind-the-scenes structure, performance, and metadata now influence not only traditional search rankings but also how content is selected and presented by AI-powered engines. In this article, we explore how to optimize your site’s technical setup for the age of generative search. We’ll cover maintaining crawlability (including new AI-specific crawlers), using structured data and clean HTML to speak clearly to algorithms, ensuring fast and user-friendly page experiences, and employing new techniques to control if and how your content appears in AI-generated answers.

The goal is to give AI and search engines every possible clue to understand and trust your site – while avoiding pitfalls that could hide your content or misrepresent it. Technical SEO for generative search is largely an extension of core SEO best practices, but with fresh nuances. Think of it as laying a solid, machine-friendly foundation beneath your high-quality content. If content is king, technical SEO is the architect that builds the castle – and now that castle must be welcoming to AI “visitors” as well as human ones. By the end of this article, you’ll have a clear action plan on how to tune your site’s technical elements (from robots.txt to HTML tags to page speed) so that both search engine crawlers and large language models can access, interpret, and feature your content accurately. Let’s dive in.

Ensure Crawlability and Access

The first step in technical SEO – whether for classic search or generative AI – is to ensure your site can actually be crawled and indexed. Crawlability means that automated bots (search engine spiders and now AI crawlers) can discover and fetch your content easily. If your content isn’t accessible, it won’t appear in search results or AI answers, no matter how great it is. Thus, maintaining clean, open access for reputable crawlers is critical.

Open Your Site to Search and AI Crawlers

Review your robots.txt file and other bot controls to make sure you’re not inadvertently blocking important crawlers. Traditional search engines like Google, Bing, and others should of course be allowed to crawl key pages (unless there’s a specific reason to block something). In the context of generative AI, new crawlers have emerged that website owners should consider.

For example, OpenAI introduced GPTBot in 2023, which seeks permission to scrape web content for training models like ChatGPT ( ^[1] ). Google similarly announced a user agent called Google-Extended to let site owners opt out of content being used for Google’s AI (such as Bard or the Gemini model) ( ^[2] ).

Importantly, blocking these AI-focused crawlers is a strategic choice. If you allow GPTBot and similar bots, your content may be included in the training data of future AI models, potentially giving your brand visibility in AI responses. If you disallow them, you’re signaling that your content shouldn’t be used for AI training – which might protect your content from misuse, but also means AI models may “know” less about your site.

For instance, The New York Times and other major publishers updated their robots.txt to block GPTBot and Google’s AI crawler in late 2023 amid concerns about uncompensated use of content ( ^[3] ) ( ^[4] ). According to an analysis in September 2023, about 26% of the top 100 global websites had blocked GPTBot (up from only ~8% a month prior) as big brands reacted to AI scraping ( ^[5] ). This blocking trend peaked around mid-2024 when over one-third of sites were disallowing GPTBot, including the vast majority of prominent news outlets. However, by late 2024 the tide shifted – some media companies struck licensing deals with AI firms and the block rate dropped to roughly one-quarter of sites ( ^[6] ).

In other words, many sites initially hit the brakes on AI crawling, but some have since opened back up as the ecosystem evolved. There’s no one-size-fits-all answer here.

Online marketers must weigh the pros and cons for their specific situation. If your goal is maximum exposure and you’re comfortable with your content being used to train or inform AI, then keeping the welcome mat out for GPTBot, Google-Extended, and similar bots is wise. On the other hand, if your content is highly proprietary or you have monetization concerns, you might choose to restrict these bots until clearer compensation or control mechanisms are in place. Just keep in mind that opting out only affects future AI training – if an AI model has already ingested your content, blocking now won’t make it “unlearn” it ( ^[1] ).

And not every AI provider announces their crawler or respects robots.txt; by blocking the ones that do (OpenAI, Google), you’re at least signaling your preference to the major players (and perhaps to any others who choose to honor these signals) ( ^[7] ) ( ^[8] ).

From a practical standpoint, auditing your robots.txt is easy and important. This text file, located at your domain’s root (e.g. yourwebsite.com/robots.txt ), tells crawlers what they can and cannot access.

To allow OpenAI’s GPTBot full access, you could add rules like this:

plaintext # Allow GPTBot to crawl the entire site User-agent: GPTBot Allow: / 

If instead you decide to block an AI crawler, you’d use Disallow .

For example, to block GPTBot or Google-Extended (Google’s AI crawler) across your whole site, your robots.txt would include:

plaintext User-agent: GPTBot Disallow: / User-agent: Google-Extended Disallow: / 

The snippet above outright forbids those bots from crawling any page on your site ( ^[3] ) ( ^[4] ). Y

ou can also be more granular – for instance, allow them on most of your site but disallow in specific sections (like /private/ or a members-only blog). Just list the appropriate path under a Disallow for that user agent. 

Remember: robots rules are public (anyone can view them), and compliance is voluntary. OpenAI and Google have stated their bots will follow these directives, but other AI projects might not. Still, it’s currently the best tool site owners have to request not to be scraped. Aside from these new AI-specific entries, ensure you’re not unintentionally blocking major search engine bots (Googlebot, Bingbot, etc.) in your robots.txt .

Generative AI features like Google’s SGE (Search Generative Experience) draw on pages indexed in Google’s search index ( ^[9] ). If Googlebot can’t crawl and index a page because of a Disallow or other barrier, that page definitely won’t appear as a link in an AI overview.

In fact, Google has confirmed that to be eligible for AI overview inclusion, a page must be indexed and have a snippet in normal search ( ^[9] ). So double-check that your important pages are crawlable (no erroneous noindex tags or disallow rules). Also verify that your site’s CDN or firewall isn’t blocking common bots – sometimes cloud security services can inadvertently serve up CAPTCHAs or blocks to non-human visitors. Google’s guidance for AI features emphasizes “ensuring that crawling is allowed in robots.txt, and by any CDN or hosting infrastructure” for your content ( ^[10] ).

Sitemap and Site Structure for Discovery

Even with proper robots.txt settings, you want to make it as easy as possible for bots (search or AI) to find all your key content. This is where XML sitemaps and internal linking come in.

An XML sitemap is a file listing the URLs on your site you want indexed, which you can submit to Google Search Console and other engines. This helps crawlers discover pages that might not be readily found through your navigation alone. Maintaining an up-to-date sitemap is still a recommended practice – it’s part of good technical SEO hygiene, ensuring no content is orphaned.

Likewise, robust internal linking remains important. Bots follow links to navigate your site. A well-structured site with logical internal links (e.g. from category pages to sub-pages, from blog posts to related posts, etc.) will be easier for crawlers to fully traverse ( ^[10] ). For AI purposes, internal links also provide context – they help search engines (and potentially LLMs) understand relationships between pieces of content.

For example, linking your glossary page to various articles might signal to an AI that you have a definition of a term, which could be useful in an answer. In an earlier post we discussed content hubs and topic clustering; from a technical angle, implementing those via internal links and taxonomy is key so that crawlers can see the full picture of your content network.

One emerging idea, relevant to both crawlability and AI, is the proposal of an llms.txt file as a companion to robots.txt . Introduced in late 2024, llms.txt is a concept by which site owners would create a special file to guide large language models to the most important information on the site ( ^[11] ) ( ^[12] ).

Unlike a sitemap (which lists all pages for search indexing), an llms.txt would provide a curated, markdown-formatted overview of the site’s content specifically for AI consumption. The rationale is that LLMs have limited context windows and struggle to parse complex web layouts; a concise markdown guide can point the AI to key pages or provide summaries.

For instance, llms.txt might include a brief description of your site and direct links to your documentation, FAQs, product pages, etc., in a simplified format. This standard is still in proposal stage, not widely adopted yet, but it signals how the industry is thinking about making websites more LLM-friendly at the source. Forward-thinking organizations (especially those with extensive documentation or data) may consider experimenting with such a file to see if it improves how AI agents interact with their content. BrandScanner helps online marketing professionals to create a valid llms.txt file that is custom-tailored for their website with a few clicks. Also, it offers a powerful tool to check an existing llms.txt file for compliance.

At minimum, staying aware of initiatives like llms.txt will prepare you to take advantage once search engines or AI tools start looking for it. In summary, don’t lock your content behind technical barriers. Let legitimate bots in, and give them a map (sitemaps, internal links, perhaps future LLM guides) to roam your site easily. This foundational step is crucial: no amount of content optimization will matter if bots can’t access your pages in the first place. By being crawl-friendly, you ensure your carefully crafted content is visible to both the index and the algorithms that generate rich answers on top of that index.

Structured Data and Schema Markup

Structured data (schema markup) is a technical SEO cornerstone that has gained renewed significance in the era of generative AI search. By adding structured data to your HTML, you provide explicit clues about the meaning of your content – clues that search engines and AI can easily parse. In traditional SEO, schema markup has been used to enable rich results (like star ratings, recipe info, FAQ dropdowns in Google’s SERPs). Now, those same structured annotations can help your content become the building blocks of AI-generated answers and overviews.

Speaking the AI’s Language with Schema

Think of schema markup as a way of translating your human-friendly content into a format that machines can understand unambiguously. For example, if you have a product page, adding Product schema (with fields for name, price, availability, aggregate rating, etc.) tells search engines exactly what the key attributes of the product are.

If you have an article, Article schema can specify the headline, author, publish date, etc. This metadata gives context that might not be immediately obvious from the raw text. Google has stated plainly: “You can help us by providing explicit clues about the meaning of a page by including structured data on the page.” ( ^[13] ) In one Google example, they mention that on a recipe page, schema can highlight the ingredients, cooking time, temperature, calories, and so on ( ^[13] ).

In short, schema helps ensure that what your page is about is crystal clear to a machine. During 2023–2024, Google continued to invest in structured data. Notably, in early 2024 Google added support for product variant schema (to better understand product options) and introduced documentation for structured data carousels that appear within SGE results ( ^[14] ). These moves indicate that Google’s generative AI overview is utilizing schema.org data where available.

In fact, SEO experiments have found that pages with comprehensive schema are more likely to be trusted and used by Google’s AI. As one agency noted, “A properly marked up site helps you to appear in AI answers by telling search engines what your data means (not just what it says) so they can accurately interpret the content.” ( ^[15] )

Structured data essentially acts like a fact highlighter, potentially making it easier for AI systems to extract relevant details or to choose your page as a source. Beyond Google, other AI-driven platforms also appreciate structured info. Bing’s AI chat, for instance, often cites sources and could benefit from schema to identify specific answers (like FAQs or how-to steps).

Likewise, tools like Perplexity AI – which provides citation-rich answers – might more readily surface a site that clearly marks up Q&A or other useful content chunks. Even if an AI doesn’t explicitly read the JSON-LD on your page, remember that the search indexer does, and the AI often works off the search index. So better understanding by the indexer can translate to better inclusion by the AI.

Types of Schema to Implement

There are hundreds of schema types, but you don’t need to use them all – focus on those most relevant to your content.

Here are some high-impact schema types for typical sites and how they help: 

• Organization : Mark up your organization’s details (name, logo, contact info, social links). This reinforces your brand identity to search engines ( ^[16] ). It’s especially useful if an AI is answering a query about your company or needs to pull your logo or address for an overview. 
• Breadcrumb : Provides the page’s position in your site hierarchy (e.g. Home > Category > Subpage) ( ^[17] ). This helps search/AIs understand site structure and can be used to display breadcrumb navigation in results. 
• Article/BlogPosting : For content pages, this defines the headline, author, publish date, article body, etc. ( ^[18] ). In an AI context, clearly indicating the author and date can lend credibility (e.g. SGE might show the date to users). It also ties into Google’s emphasis on experience/expertise (E-E-A-T) by linking content to author entities. 
• Product : Critical for e-commerce, this schema defines product name, description, price, currency, availability, reviews, etc. ( ^[18] ). If someone asks an AI “What’s the price of [Product]?” or “Is [Product] in stock?”, an AI overview might draw on this info. Indeed, Google’s AI snapshots have been seen displaying product specs and images, likely informed by structured data. 
• FAQPage : Mark up frequently asked questions and answers on your page ( ^[19] ). This one is very powerful – many sites have an FAQ section, and marking it up can make you eligible for FAQ rich results. Moreover, an LLM can easily use a Q&A pair from your markup to directly answer a user’s question (with attribution). If ChatGPT or Bard is asked a question that exactly matches one of your FAQs, there’s a chance your Q&A could be used verbatim if the AI has access to that information. 
• HowTo : If your content explains how to do something in steps, use HowTo schema to mark the steps, tools required, etc. Google’s SGE has shown step-by-step answers for how-to queries, often sourced from well-structured how-to pages. The HowTo schema makes it straightforward for an AI to identify the ordered steps on your page. 
• LocalBusiness : For businesses with physical locations, this schema can provide your address, opening hours, geo-coordinates, etc. ( ^[20] ). An AI assistant answering “Find me a hardware store open now” could rely on such info. 
• Person / Author Profile : Use Person schema for your authors or notable individuals on your site ( ^[20] ). This can reinforce expertise by linking content to author profiles (with details like their title, bio, sameAs links to social media). Google’s guidance around E-E-A-T suggests that clearly identifying authors and their credentials can improve content trust – something especially relevant if AI is summarizing advice or info from your page.

And the list goes on – Recipe schema for recipe sites, Review schema for review content, Event schema for event listings, etc.

The key is to identify the schema that aligns with your content and implement it consistently. BrandScanner offers several helpful tools to create compliant Schema.org markup for FAQ and HowTo schema quickly and reliably.

Do an audit of your site: if you have a bunch of pages that could be marked up as FAQs, do it; if you have product pages without Product schema, add it. Each piece of structured data is another hint to the algorithms about what your page offers.

To illustrate, here’s a small example of FAQ schema in JSON-LD (a common format for adding schema):

{
   “@context”: “https://schema.org”,
   “@type”: “FAQPage”,
   “mainEntity”: [
    {
         “@type”: “Question”,
         “name”: “What is Example?”,
         “acceptedAnswer”: {
               “@type”: “Answer”,
               “text”: “Example is a placeholder FAQ item.”
          }
   },
   {
   “@type”: “Question”,
   “name”: “How does it work?”,
   “acceptedAnswer”: {
      “@type”: “Answer”,
      “text”: “It works by providing structured Q&A data for search engines and large language models.”
      }
   }
   ]
}

In the above snippet, we clearly label a question and its answer. A search engine or AI parsing this knows exactly that this text is a question (“How do I optimize my site for AI search results?”) and the answer to that question. In a scenario where, say, Google’s Bard is compiling an answer about “how to optimize for AI search,” it might pick up this Q&A from a site if it finds it relevant, thanks to the precise labeling. (Of course, having this markup doesn’t guarantee you’ll be featured, but it puts you in the game.)

Structured data also contributes to your site’s authority and trust in the eyes of algorithms. A well-marked up site tends to be seen as well-maintained and transparent. As an SEO expert pointed out, “A well marked-up site is more trusted by Google than a poorly marked-up site. This is because Google can quickly and easily verify your site if you provide links to external reviews, social channels etc. through schema.” ( ^[21] ). In other words, adding schema like Organization with your social media profiles, or Product schema with real review data, helps connect the dots for Google’s knowledge graph. This can only help your content’s chances of being selected by an AI summary that values authoritative, well-sourced information.

Schema Markup Best Practices in 2025

When implementing schema, follow these best practices to get the most benefit: 

1. Ensure accuracy and consistency : The structured data must match what’s on the visible page. Don’t mark up a product price of $19.99 if your page says $24.99, for example. Inconsistent or erroneous schema can backfire (search engines might ignore it or even penalize gross discrepancies). Google explicitly requires that schema reflect the page’s actual content ( ^[22] ). 
2. Use JSON-LD format when possible : JSON-LD is Google’s recommended format because it’s easy to add without altering HTML elements. It goes in the <head> or anywhere in the HTML. Other formats like microdata or RDFa also work but can be messier to implement. 
3. Validate your markup : Use Google’s Rich Results Test or Schema.org’s validator to check that your JSON-LD has no syntax errors and is pulling the intended values from your page. Also, monitor Google Search Console for any Structured Data errors or warnings. 
4. Stay up-to-date with schema types : Schema.org periodically updates with new types/properties (e.g. the Product variant expansion). Keep an eye on SEO news or Google’s announcements for new supported schema that might give you an edge. For instance, if you run an e-commerce site and Google starts supporting a new ShippingDetails schema for AI shopping results, you’d want to implement that sooner than later. 
5. Prioritize high-impact pages : If you have a huge site, adding schema everywhere can be daunting. Focus on pages that drive your business goals and are likely to be used in AI answers. Typically, these are informational pages (for question answering) and key product/service pages. You can gradually expand coverage, but make sure the most important content is marked up first. 
6. Leverage schema for multimedia : Generative search is not just about text – Google’s AI Overviews can include images and videos. You can use schema to provide context for media too. For example, ImageObject schema can describe an image (caption, license, creator), and VideoObject can do similar for videos.

This metadata could become more important as AI results get more visual. In essence, adding structured data is like creating an enhanced resume for your content – it highlights all the key points in a way a machine can quickly grasp. As AI continues to evolve, feeding it structured, unambiguous information will only become more beneficial. Make your site scream its meaning, and you increase the odds that an AI will pick up on your content and present it to users in rich new ways.

Semantic HTML and Readability

While structured data is one layer of optimization, the very structure of your HTML content – the headings, paragraphs, lists, and other elements you use – also plays a huge role in how AI systems interpret and excerpt your site. Semantic HTML refers to using HTML elements according to their meaning (e.g. <h1> for the main title, <h2> for subsections, <article> for a standalone content unit, <ul> for a list, etc.), rather than just for visual formatting. Clean, semantic HTML combined with a clear writing style can make your content “dense with meaning” and easy for large language models to digest.

Structure is as Important as Keywords

In the old days of SEO, one might focus on sprinkling keywords in the text. In the AI era, it’s more about structuring your information logically. Large language models don’t simply scan for keywords; they ingest the page and build an understanding from the sequence of words and how the content is organized ( ^[23] ). One SEO expert explains that LLMs like GPT-4 or Google’s Gemini examine things like “the order in which information is presented, the hierarchy of concepts (which is why headings still matter), formatting cues like bullet points, tables, [and] bolded summaries.” ( ^[24] ). In other words, the model pays attention to your content’s outline and emphasis to figure out what’s important.

If your page is a jumbled wall of text, an AI might struggle to find a clear answer or may misinterpret which parts of the text are most crucial. Consider an AI summarizing a lengthy article. How does it decide what the key points are? Likely, it will give extra weight to text that is prominent or structurally significant : titles, headings, list items, the opening sentences of a paragraph (which often contain the topic sentence), etc.

If you use headings and subheadings effectively, you’re essentially giving the AI a mini road-map of your content. For example, an <h2>Benefits of Solar Panels</h2> followed by a concise paragraph and a bulleted list of benefits is far more accessible to an AI (and a human) than a page of unstructured paragraphs burying those benefits in fluff.

In fact, well-structured content can outrank or outperform a keyword-stuffed page in AI results; “poorly structured content – even if it’s keyword-rich and marked up with schema – can fail to show up in AI summaries, while a clear, well-formatted blog post without a single line of JSON-LD might get cited or paraphrased.” ( ^[25] ).

This underscores that content architecture is king. Schema is helpful but cannot compensate for a lack of clarity in the content itself. To optimize for this, write and format your content with both readers and AI in mind: 

1. Use a logical heading hierarchy : There should ideally be one <h1> (the page title), and then <h2> for main sections, <h3> for subsections, and so on. Each section should stick to a single topic or idea, as if you were writing an outline. This not only helps readers scan, but ensures an AI summary can pick out the section relevant to a particular question. For instance, if someone asks “What is the process to do X?” and your article has a section <h2>How to Do X: Step-by-Step</h2> , an AI can jump straight to those steps. 
2. Write descriptive headings : Instead of clever puns or vague headings, be straightforward. A heading like “10.3 Semantic HTML and Readability” (like we used above) is clear about the topic. If this were a vague heading like “The Secret Sauce,” an AI might not glean what that section covers until parsing all the text. Descriptive headings (potentially with relevant keywords) improve comprehension for models and humans alike ( ^[26] ). 
3. Keep paragraphs and sentences concise : Long, run-on paragraphs can dilute meaning. Aim for paragraphs that convey one idea and aren’t overly long (roughly 3-5 sentences each is a good rule of thumb). This creates natural pausing points and makes it easier for an LLM to extract a self-contained nugget of information from a paragraph without needing excessive context. Notice how in this post, most paragraphs are reasonably short – this is intentional for readability and excerptability. 
4. Use bullet points and numbered lists wherever appropriate. Lists are fantastic for both visual and algorithmic consumption. They break complex information into digestible chunks. For AI, a list clearly indicates a set of related points or steps. If a user asks “What are the main features of product Y?” and your page has a bullet list of features, an AI can easily turn that into a concise answer. Google’s generative search often presents answers in list form when the source content is in a list. In our experience, “Google AI Overview prefers well-structured, skimmable content. Ensure your articles include clear headings, short paragraphs, bullet points, [and] numbered lists for quick scanning.” ( ^[27] ). 
5. Highlight key information : If you have critical facts, definitions, or takeaways, consider using bold or italics to make them stand out (sparingly, when truly warranted). An AI model might notice emphasis. Similarly, a short summary sentence in bold at the start of a section (sometimes called a TL;DR or key point) can telegraph the main idea. Some websites put an important conclusion in bold text – which could be the line an AI chooses to quote directly. 
6. Use tables for structured data comparisons : When you have data or a comparison that fits a table format, using an HTML <table> can be helpful. Tables explicitly organize information into rows and columns. For instance, a pricing comparison table or a specs comparison (Feature X vs Feature Y) could be read by AI to pull a specific comparison point. (Do ensure to include a summary in text as well, since extremely tabular data might be skipped by some models that focus on text.) 
7. Include alt text for images (briefly, as a semantic point): While images themselves aren’t directly “readable” by text-based LLMs, the alt text you provide is. And with multimodal models emerging, having descriptive alt text ensures the AI knows what an image contains. For example, if you have a chart showing data, an AI like Bing’s image interpretation might read the alt text/caption to understand it.

Below is a simplified example of well-structured HTML content:

<article> <h1>Guide to Solar Panel Installation</h1> <p>Installing solar panels can significantly reduce your energy costs. This guide outlines the steps and important considerations.</p> <h2>Benefits of Solar Panels</h2> <p>Solar panels offer multiple benefits for homeowners:</p> <ul> <li><strong>Lower electricity bills:</strong> Generate your own power and rely less on the grid.</li> <li><strong>Environmental impact:</strong> Solar energy is renewable and clean, reducing your carbon footprint.</li> <li><strong>Increased home value:</strong> Homes with solar installations often appraise higher.</li> </ul> <h2>How to Install Solar Panels</h2> <p>Here is a step-by-step overview of the installation process:</p> <ol> <li><strong>Assess your roof:</strong> Ensure it has structural integrity and good sun exposure.</li> <li><strong>Choose a system:</strong> Select solar panel type and inverter based on your energy needs.</li> <li><strong>Hire a professional (recommended):</strong> A certified installer will mount panels and connect the system safely.</li> <li><strong>Inspection and connection:</strong> Get the system inspected and connected to the grid per local regulations.</li> </ol> <h3>Common Mistakes to Avoid</h3> <p>Be aware of these pitfalls during installation:</p> <ul> <li>Not checking local permits and regulations.</li> <li>Ignoring the angle and direction of panels (affects efficiency).</li> <li>Skimping on quality for cost – cheaper panels may underperform long-term.</li> </ul> </article> 

In this snippet, the content is organized with meaningful headings (“Benefits of Solar Panels”, “How to Install Solar Panels”, “Common Mistakes to Avoid”). Important phrases are bolded to draw attention. Lists are used for benefits, steps, and mistakes, breaking the info into clear points. An LLM reading this would have an easy time identifying, say, the benefits of solar panels if asked, or enumerating the installation steps, because the HTML layout itself delineates those pieces. This is far better than a single giant paragraph about installation buried somewhere.

Write in a Clear, Conversational Style

Semantic HTML deals with the structure; equally important is the language style you use. Generative AI is essentially trying to emulate human answers. If your content is written in a plain, conversational manner that directly addresses common questions, it’s more likely to be selected and reproduced by an answer engine.

A few tips on style and clarity: 

• Address likely user questions in the text: from a writing perspective, it helps to pose and answer questions within your content. For example, include an explicit question as a subheading (“How much money can solar panels save annually?”) followed by the answer. This Q&A style content (even outside of an FAQ section) makes it trivial for an LLM to match a user’s question to your answer. In contrast, if the answer is hidden in a long narrative, it might be overlooked. 
• Use natural language and define jargon: Content that reads in a straightforward way will be more quotable by AI. If you must use technical terms, define them briefly – not only is that good for users, it also helps the AI not to misinterpret specialized terms. 
• Avoid unnecessary fluff: While a human reader might appreciate a bit of storytelling, an AI summarizer is looking for facts and direct statements to extract. It’s fine to have a personable tone, but try not to bury key facts in metaphor or overly flowery language. A generative AI might miss the nuance or, worse, mis-summarize it. 
• Ensure each paragraph has a topic sentence: A well-crafted first sentence of a paragraph that summarizes the point acts as a signal to an AI. If the rest gets truncated, at least the main idea was clear up front. 
• Maintain context : LLMs have limits on how much of the page they can use at once. If your page is very long, consider breaking it into sections or pages (perhaps with jump links) so that each addresses a subtopic clearly. Multi-turn AI conversations (like in Bing’s chat mode or others) might drill down into subtopics – if your content is modular, it fits these follow-up questions well. 
• Use examples or analogies carefully : These can clarify for humans, but ensure you explicitly state the point the example is illustrating. An AI might otherwise repeat the example literally without the context, which could be odd. (For instance, if you say “Think of schema as the DNA of your site…” in an article about schema, Bard might respond with that analogy verbatim to a user – which may or may not be the ideal answer.)

In summary, think of your page as an outline of answers. The better organized and clearer it is, the easier you make an AI assistant’s job. This not only improves your chances of being featured, but also reduces the risk of an AI misinterpreting or misrepresenting your content. By using semantic HTML and a reader-friendly writing style, you essentially future-proof your content for both human readers and AI algorithms that thrive on clarity and structure ( ^[24] ) ( ^[25] ).

Page Experience and Performance

No matter how great your content and markup are, a poor user experience can undermine it all. Page experience – which includes factors like site speed, mobile-friendliness, security, and lack of intrusive interstitials – remains a priority in the generative era. Google has repeatedly affirmed that the same signals used in regular search ranking continue to apply for AI features ( ^[28] ) ( ^[29] ).

Fast, smooth websites not only rank better; they also integrate more seamlessly with AI systems that fetch and display content. In this section, we’ll look at why performance and UX still matter for GEO (Generative Engine Optimization) and how to ensure your site meets modern standards.

Core Web Vitals and Speed: Still Critical

Google’s Core Web Vitals – a set of metrics for loading performance, interactivity, and visual stability – are essentially a quantified measure of user experience. They include Largest Contentful Paint (LCP), Cumulative Layout Shift (CLS), and (recently replacing First Input Delay) Interaction to Next Paint (INP).

As of 2025, Google treats these vitals as a key element of its page experience criteria ( ^[30] ) ( ^[31] ). In plain terms, Google wants websites to load quickly, not jump around as they load, and respond promptly to user input. Sites that meet the thresholds for “good” Core Web Vitals are likely to have a minor ranking advantage, and more importantly, they keep users happy. Why does this matter for generative search?

Several reasons: 

1. User Behavior : Imagine a user sees an AI-generated answer with a source link to your site. If they click your link and your page loads slowly or is clunky, the user might bounce quickly. Not only have you lost that engagement (and potential conversion), but if this happens frequently, it could indirectly signal to Google that your page isn’t a satisfying result. In the context of SGE, Google wants to send users to helpful sites. It stands to reason that a fast site with a good experience is more likely to be deemed “helpful” than a sluggish site, all else being equal (even if just through correlation of other ranking signals like bounce rate or time on site). 
2. AI Content Fetching : Some AI agents fetch page content in real-time when generating answers (for example, Bing’s chat mode will visit webpages to quote them, and tools like Perplexity load pages to pull facts). If your site is extremely slow or has aggressive anti-bot measures, the AI might time out or fail to retrieve the info. A fast-loading site ensures that when an AI or bot pings your page, it can quickly get the content and move on. One can imagine that if Bing’s crawler encounters timeouts on your site, it might avoid using it as a source in the future due to reliability issues. 
3. Mobile-First Users : The majority of searches are on mobile devices, which often have slower connections. A page that loads fast on mobile (and is mobile-friendly) is going to serve those users better when they click through from an AI result on their phone. If an AI result encourages a user to visit your page, you want the transition to be frictionless. Google’s page experience guidelines explicitly emphasize mobile responsiveness and performance ( ^[32] ) ( ^[33] ). 
4. Future AI Integration : As AI features might become more directly integrated into browsers or assistant devices, having a lightweight page can facilitate quick previews or snippet generation. For example, if a voice assistant of the future fetches your page to read an answer aloud, you’d want it to fetch, parse, and start delivering that answer near-instantly.

So, speed matters. Let’s drive the point home with some stats. Users are impatient: 53% of people will leave a mobile page if it takes longer than 3 seconds to load ( ^[34] ).

Furthermore, fast sites have a clear business advantage. One study found that for B2B websites, a site that loads in 1 second had a conversion rate 3 times higher than a site that loads in 5 seconds (and 5 times higher than a site that loads in 10 seconds) ( ^[35] ).

The relationship between load time and conversions/bounce is dramatic. Users reward speed. Every additional second of loading can sharply reduce the likelihood of engagement or purchase. This is why Google continues to underline performance. In 2025’s page experience update, Google’s checklist for webmasters is to “perform well on Core Web Vitals” and keep improving speed via techniques like optimized scripts or server-side rendering ( ^[30] ) ( ^[36] ).

In short, speed is a feature, not just a technical detail. To ensure your site meets these standards: 

• Measure your Core Web Vitals using tools like Google PageSpeed Insights, Lighthouse, or the Core Web Vitals report in Search Console. Identify if LCP, CLS, or INP are in the “needs improvement” or “poor” range on either mobile or desktop. 
• Optimize your assets : Compress images (they are often the biggest contributors to slow LCP), use modern image formats (WebP/AVIF), and serve images at appropriate sizes. Minify and combine CSS/JS files where possible, and defer loading of any scripts not needed for initial paint. 
• Leverage browser caching and CDNs to reduce repeat load times and serve content from geographically closer servers. 
• Use performance-enhancing techniques : such as lazy-loading images (via the loading="lazy" attribute on <img> tags for below-the-fold images), preloading critical resources, and removing render-blocking resources. For example, adding loading="lazy" in an image tag will delay loading that image until it’s needed, speeding up initial render: html <img src="/images/large-diagram.png" alt="Architecture Diagram" loading="lazy"> 
• Mobile-first design : Ensure your responsive design is efficient. Avoid huge CSS frameworks or heavy libraries if not needed. Test on real devices or emulators to see how your site performs on a typical 4G connection. 
• Avoid heavy client-side rendering for basic content : If your content is primarily text and images (like a blog), you likely don’t need a massive single-page app framework. Server-rendered HTML is fast and SEO-friendly. If you do use client-side frameworks, use dynamic hydration or static generation to send down HTML first (so the user isn’t staring at a blank screen). 
• Monitor and iterate : Performance optimization is ongoing. Use real-user monitoring (e.g. Chrome User Experience Report data accessible via tools) to see how changes impact actual users over time.

Other Page Experience Factors

Performance is a big chunk of page experience, but not the only part. Google’s page experience update (and common sense) include several other factors: 

• Mobile-Friendly, Responsive Design : As noted, your site should work well on mobile devices. This isn’t optional – Google moved to mobile-first indexing years ago. Responsive design (using CSS media queries to adapt layout) is the preferred approach ( ^[33] ). Test your pages in different screen sizes. Text should be readable without zooming, buttons/tap targets should be easily clickable, and horizontal scrolling should be avoided. If your desktop site is great but the mobile view is broken or hard to use, not only will users leave, but Google’s ranking for mobile searches (which feed SGE on mobile) will suffer. 
• HTTPS Security : Serving your site over HTTPS is a must (and has been a lightweight ranking factor for a long time). In 2025, Google treats HTTPS as table stakes – it won’t boost you just for being HTTPS, but not having it could hurt trust and rankings ( ^[37] ). Also, AI scrapers likely skip non-HTTPS sites or could flag them as less trustworthy. Always redirect HTTP to HTTPS, and consider HSTS to enforce it. 
• Avoid Intrusive Interstitials/Pop-ups : If your content is hidden behind a giant popup (like a newsletter sign-up or an app install banner), it frustrates users. Google has guidelines against intrusive interstitials, especially ones that cover content on page load. For AI, think about it this way: an AI trying to read your page might get stuck or read the wrong text if a popup dominates the HTML. Even if the AI can bypass it, a user clicking through won’t be happy to find they have to close a modal to see the info promised. Keep any required interstitials small, or delayed, or better yet, use subtle banners. Google explicitly says to avoid overlays that take up too much screen, especially above-the-fold ( ^[38] ) ( ^[39] ). This includes things like cookie consent banners – try to use minimal ones that don’t block content (or utilize the browser’s built-in mechanisms where possible). 
• Ad Experience : Sites overloaded with ads, especially at the top of the page, create a bad user experience. Google’s “page layout algorithm” and subsequent guidance penalize sites that shove content far below ads ( ^[38] ) ( ^[40] ). If a user comes from an AI answer expecting to see the solution and instead they get a full-screen ad or five ads before the content, they’ll bounce. Also, an AI summarizer might inadvertently read ad code or irrelevant text if the page isn’t well-structured. Keep ads to reasonable levels, and make sure they’re labeled and separated in the DOM (so, for instance, use dedicated containers or iframes that an AI can skip over as not part of main content). 
• Consistent Layout (No Jank) : This relates to CLS (layout shift). Ensure that your CSS and media dimensions are set so that the page doesn’t jump around as it loads. Unexpected shifts can not only annoy users but might confuse an AI trying to capture a screenshot or parse content during load. 
• Enable Prompt Content Display : When a user clicks through from an AI, they often have a specific query in mind (maybe even a specific snippet that was referenced). Consider using techniques like fragment URLs or highlighting. For instance, some search features scroll the user to the quoted text or highlight it (SGE was experimenting with this). You can’t fully control that, but having clear anchor links for sections could allow a browser or AI agent to jump to the relevant part (for example, a table of contents with anchor links to sections). 
• Monitor with Real Users : Keep an eye on your analytics for bounce rates, time on site, etc., especially for traffic coming from new AI features. If you see unusual behavior (like very short time-on-page for AI-originating clicks), it might hint that users aren’t finding what they expected (or page load issues). This feedback loop can inform further UX tweaks.

The bottom line is that user experience principles haven’t changed – if anything, they’re reinforced. Google’s own documentation ties helpful content with good page experience, noting they are “fundamentally connected” ( ^[41] ). The companies building AI search want to provide good experiences, so they will naturally favor content from sites that do the same. A great technical SEO knows that speed and UX improvements not only boost SEO, but also conversion and user satisfaction.

It’s truly a win-win-win for users, search engines, and your business metrics. By investing in page experience, you ensure that when your content is surfaced – either directly in an AI answer or via a link – the user’s journey doesn’t falter. They get the information faster, they trust your site more, and they’re likelier to stick around. In a world where attention is gold, a snappy, pleasant website is your chance to shine after earning that AI-generated click.

Preventing AI Misinterpretation and Misuse

A unique challenge with generative AI is that it might reinterpret or repurpose your content in ways you didn’t intend. While traditional SEO is mostly about getting indexed and ranked, GEO also involves guiding how AI systems use your content. This includes preventing snippets from being taken out of context, avoiding hallucinations (where the AI might mix up facts), and generally ensuring your content is represented accurately.

In this section, we’ll discuss technical measures to control or influence how AI “reads” your pages – from special meta tags that block AI summaries to using clear markup for quotes or code to reduce misinterpretation. We’ll also touch on emerging standards and ethical considerations for content usage.

Marking Content for Clarity (Quotes, Code, and More)

One straightforward way to avoid AI misinterpretation is to clearly delimit different types of content on your pages. By using the appropriate HTML elements for quotes, code, definitions, etc., you give the AI parser cues about the nature of that text. This can prevent, for example, a user comment or a sarcastic statement from being read as the site’s official stance.

Consider a scenario: You run a forum or a Q&A site. A user posts an incorrect answer or a controversial opinion, and your page displays it. If that user content isn’t distinguished in markup, a search AI might scrape the page and present the user’s statement as a fact attributed to your site. That could be damaging or just inaccurate.

By wrapping such content in a <blockquote> with a citation of the user, or marking it as a user-generated section, you at least signal “this is a quote/opinion”. Google’s indexing system might treat it differently (for instance, Google often ignores or devalues text in <blockquote> for snippet purposes if it’s clearly a quote from elsewhere). Likewise, an LLM might be more likely to attribute the quote properly or skip it if not relevant to a direct question.

Similarly, for technical content, using the <code> or <pre> tags for code snippets or command-line outputs is critical. Not only does this preserve formatting, but it tells any AI or parser that “this text is code or technical output”. The AI then is less likely to confuse it with prose. For example, if you have a line in your tutorial that shows an error message or a piece of JSON, putting that in a code block ensures the AI doesn’t accidentally mingle it with your explanatory text.

It might also choose to display it verbatim (with a monospace font) if providing an answer. For instance:

<p>When we ran the test, we encountered the following error:</p> <pre><code>ERROR 503: Service Unavailable</code></pre> <p><em>Solution:</em> This error usually means the API endpoint is down; try again later.</p>

In the above snippet, the error message is clearly marked as code. An AI summarizing common errors would likely quote the error exactly as shown (which is what you want), and it knows the next paragraph is a solution (since it’s in normal text with perhaps emphasis on “Solution:”).

By contrast, if you had just written: “When we ran the test, we encountered ERROR 503 Service Unavailable solution: this means the API is down…”, the AI might extract something garbled. Another use of markup is for definitions or key terms. You might use <dfn> tag for defining instances (though not widely used) or simply italic/bold the first occurrence of a term and define it immediately. For example: “Generative Engine Optimization (GEO) – adapting SEO techniques for AI-driven search results.” This immediately pairs the term with its definition. If someone asks an AI “What is Generative Engine Optimization?”, there’s a tidy definition it can pull from your page.

Some advanced HTML5 elements like <aside> can mark side content, and <figcaption> can label image captions – use these appropriately so that if an AI scrapes your page, it can distinguish main content from side notes and captions. In summary, use HTML as it was intended, to semantically separate content roles. Quotes for quotes, lists for lists, headings for titles, code for code, etc. A well-structured HTML document not only looks organized, but semantically it minimizes misreads. It’s like giving AI a script with stage directions.

The model might still mess up, but you’ve done your part to clarify who is saying what and in what format.

Controlling Snippets and AI Usage via Meta Tags

While semantic HTML helps with interpretation, there are cases where you may not want your content to appear in AI-generated snippets at all, or you want to limit how much of it appears. Perhaps you run a subscription-based site and prefer not to have AI giving away your content for free, or maybe you have an page that you feel is likely to be misused if taken out-of-context.

Google has provided some tools – originally for controlling search snippets – that also apply to its generative AI snippets in Search. By using these, you can opt out or limit how your content is used in AI overviews. Key methods (for Google in particular) include the following ( ^[42] ) ( ^[43] ):

•  nosnippet meta tag – This tells Google not to show any snippet of your page in search results. Implement by adding to your HTML <head> : <meta name="robots" content="nosnippet"> . Google has confirmed this will prevent your content from being used in SGE AI overviews or featured snippets ( ^[42] ). Essentially, your page can still be indexed and ranked, but Google will only show the URL/title (no text extract). This is a blunt but effective tool if you absolutely want to avoid being summarized by Google’s AI. Keep in mind, it also removes your rich snippet in regular search, which might reduce clicks. Use it selectively.
•  max-snippet meta tag – This meta directive lets you specify a maximum character length for snippets. For example: <meta name="robots" content="max-snippet: 50"> would tell Google to only use up to 50 characters of a snippet ( ^[44] ). Setting it to 0 is effectively the same as nosnippet (no snippet at all) ( ^[44] ). This gives a bit more nuance – you could allow a short snippet but not a long excerpt. Maybe you’re okay with a one-liner appearing in AI, but not a full paragraph.
•  data-nosnippet attribute – This is an HTML attribute you can apply to specific elements in the body of your page to mark them as off-limits for snippets ( ^[45] ). For instance, <p data-nosnippet>Confidential information here.</p> ensures that particular paragraph won’t show up in Google’s results or AI answers ( ^[45] ). This is useful if 95% of your page is fine to snippet, but there’s a sensitive part (like a key takeaway that you want people to click through for, or a segment that doesn’t make sense out of context). By sprinkling data-nosnippet on those parts, you control exactly what content could be lifted. 
• X-Robots-Tag HTTP header – This is similar to the meta tags, but set at the server level. You can configure your server to send X-Robots-Tag: nosnippet in the HTTP headers for a page ( ^[46] ). It has the same effect as the meta tag. This is often used for non-HTML content (like PDFs) or if you prefer server config. For most, the meta tag approach is easier, but it’s good to know both exist. 
• Canonical tags for duplicates – If you have duplicate or very similar content on multiple pages, canonicalization helps ensure Google (and by extension its AI) knows which is the primary source ( ^[47] ). This can prevent weird cases where perhaps an AI overview pulls from a duplicate page or shows a less complete version of your content. By using <link rel="canonical" href="https://www.example.com/preferred-page"> on duplicates ( ^[47] ), you signal the original. This is a standard SEO practice, but in the AI context it’s about steering the AI to the source you want. It also helps avoid confusion if, say, you have a print view of an article – you don’t want the AI quoting the print view URL.

Let’s see a quick example of using some of these on a page:

<head> <meta name="robots" content="max-snippet: 0, noimageindex"> <!-- This would prevent text snippets and also avoid indexing images on the page --> </head> <body> <h1>Research Report on Industry X</h1> <p>Executive summary of the report...</p> <p data-nosnippet><strong>Key Findings:</strong> [The key findings are listed in the full report]</p> <p>The rest of the page content goes here...</p> </body>

In this snippet, we chose to use max-snippet: 0 for the whole page (no text snippet at all) and also noimageindex just as an example to not index images (maybe if they were proprietary charts). Additionally, we put a data-nosnippet on the “Key Findings” paragraph. This belt-and-suspenders approach ensures the most crucial part (the findings) never show up in AI – forcing users to click the page to read them – and in fact no snippet at all will show.

Alternatively, we could be less strict in the meta tag (allow some snippet) but still protect the findings paragraph. The combination is flexible. 

Important caveat : These measures currently apply mainly to Google Search and any of Google’s generative search features. Other platforms may not honor them. Bing, for instance, at one point said it would respect meta noindex and maybe nosnippet, but we don’t have as clear documentation on Bing Chat’s handling.

That said, if you block Bing’s crawler via robots, it won’t see the content at all. OpenAI’s ChatGPT browsing plugin would obey robots (and thus not see pages disallowed). But if your content ended up in the training data of GPT-4 already, nosnippet now won’t retroactively remove it. So these controls are mostly about future AI interactions and specifically things like search engine generated answers. 

Pros and Cons : Using snippet controls is a double-edged sword. On one hand, it can drive more clicks (since users can’t get the info without visiting) and protect content. On the other, your site might not be referenced by AI at all if it can’t use a snippet. Google has noted that links in AI overviews often get higher CTR than traditional results ( ^[48] ) – if you opt out of being included, you miss out on that traffic. So use these tactics thoughtfully. Perhaps you employ them on pages where you genuinely need to withhold info, but leave most pages open for AI to feature.

It’s analogous to the early days of featured snippets – some sites blocked them fearing loss of traffic, only to find they lost presence. Others embraced them and adjusted strategy (e.g., by providing just enough answer to entice a click for more detail). As a technical SEO, you should also monitor how your content appears in AI outputs. Search for your brand or content snippets on ChatGPT (with browsing or plugins), Bard, Bing, etc. If you find the AI is consistently misunderstanding or misusing your content, that might be a clue to tighten things up – maybe add data-nosnippet around the problematic bits, or add more clarifying text that eliminates ambiguity.

Emerging Standards and Keeping Control

The landscape of AI and content usage is evolving rapidly. We’ve seen the emergence of proposals like NoAI meta tags and the previously discussed llms.txt . While not yet standardized by any search engine, the “noai” directive is being promoted in some communities (especially among artists and content creators) as a way to signal “I don’t want my content used for AI” ( ^[49] ).

For example, DeviantArt introduced a <meta name="robots" content="noai"> for art pages to opt out of AI training ( ^[50] ). Some platforms like Raptive (an ad network) have added support for noai in their publisher settings ( ^[49] ). It’s important to note that these are honor-system signals – currently, there’s no legal or technical enforcement making AI companies comply universally.

OpenAI and Google’s approach (GPTBot and Google-Extended) is the more concrete opt-out for training. But we may see a broader adoption of a machine-readable “no AI usage” flag if regulations push that way. On the flip side, we might also see tags for allowing or specifically feeding AI. For instance, a hypothetical aisummary="allowed" attribute or a schema property that indicates a snippet is expressly license-free for use. The idea has been floated that publishers might label certain content as AI-summarizable. While not reality yet, being aware of these discussions means you can implement quickly if they become available.

Another thing to watch is the regulatory environment. Governments are starting to discuss mandates around AI data usage transparency. It’s possible that in the near future, AI systems could be required to provide citations for all content or to exclude content that was disallowed. If that happens, technical SEO will include ensuring your preferences (to be included or not) are clearly communicated via whatever standard is decided (be it robots.txt, meta tags, or a new protocol). 

Monitoring tools : As part of your GEO efforts, consider tools or services that track your content’s presence in AI outputs. Some startups are emerging that claim to monitor if your website is mentioned or used by AI answers. Even simple Google Alerts or searches can catch when your text appears (though AI paraphrasing makes that tricky). If you find unauthorized or unwanted usage, you may decide to adjust your technical stance (e.g., start blocking a particular bot). For instance, if an obscure AI tool is scraping you too aggressively, you could block its user agent.

Finally, educate and collaborate with your legal and content teams. Technical decisions like blocking AI bots or adding noai tags might have business implications. There’s a balance between protecting content and gaining exposure. Part of an SEO’s role now is to advise on that strategy. For example, a financial data provider might block AI to preserve their data’s value, whereas a blog seeking readership might welcome being referenced by AI for the added visibility. These aren’t just technical calls; they’re business calls enabled by technical measures.

In conclusion, while you can’t perfectly control how AI will use your content, you do have some levers to pull. Use HTML semantics to avoid misunderstandings, and use meta directives to set boundaries with major AI-enabled platforms. Keep an eye on emerging standards like noai and llms.txt – even if they’re voluntary, they indicate a direction. By staying proactive, you protect your content’s integrity and ensure your SEO strategy adapts to the AI age rather than getting run over by it.

Conclusion and Key Takeaways

Technical SEO in the generative search era is all about laying a strong, adaptable foundation for your content. The core principles haven’t radically changed – you still need to be crawlable, fast, and structured – but the stakes are higher and the nuances are new.

A quick recap of what we’ve covered in this article: 

1. Crawlability & Access: Make sure all the right doors are open. Let search engines and reputable AI crawlers index your content. Decide strategically on allowing or blocking crawlers like GPTBot and Google-Extended based on your comfort with AI training usage. Use tools like robots.txt to communicate your preferences ( ^[3] ) ( ^[4] ). And don’t forget internal links and sitemaps to guide bots through your site. 
2. Structured Data: Speak in schema wherever possible. By marking up content with FAQ, HowTo, Product, and other schemas, you make it easier for search and AI to understand and trust your pages ( ^[15] ) ( ^[51] ). Schema is your content’s metadata resume – the extra mile that could win you that featured snippet or SGE inclusion. Implement relevant schema types and keep them updated as new ones emerge (for example, keep an eye on any schema that specifically aids AI results or rich media in search). 
3. Semantic HTML & Content Structure: Structure beats stuffing. Use headings, lists, and clear formatting to make your points stand out ( ^[24] ). Think about how an AI (or a rushed reader) would scan your page. Make it easy for them to pick up the main ideas and answers. A well-structured page is future-proof – whether it’s Google’s crawler, a GPT model, or the next big AI, the logic of your content will shine through. 
4. Page Experience & Performance: Speed and UX are the unsung heroes of SEO and now GEO. Users and AI both prefer fast, user-friendly sites. Optimize those Core Web Vitals, be mobile-friendly, and avoid anything that annoys or slows down visitors ( ^[35] ) ( ^[34] ). This not only helps rankings but ensures you convert the traffic you get. If an AI cites you and the user clicks, that click is half the victory – a great page experience seals the deal. 
5. AI Control & Snippet Governance: You have tools to prevent or shape how your content appears in AI answers. Use them judiciously. Meta tags like nosnippet or attributes like data-nosnippet can keep sensitive info out of AI overviews ( ^[42] ) ( ^[45] ). Proper HTML markup (for quotes, code, etc.) can reduce misinterpretation. Stay informed on new developments like the noai directive ( ^[49] ) or llms.txt ( ^[11] ) – they’re hints of a more structured future where content creators can explicitly signal AI usage rights and guidance. For online marketing professionals, the takeaway is that technical SEO and content strategy are two sides of the same coin.

High-quality, E-E-A-T-rich content (as discussed in earlier posts) needs a technically sound platform to truly succeed in generative search. You want your content not just to exist, but to be understood correctly and delivered optimally by the new generation of search tools.

As you advise companies and work on websites, instill a mindset of “AI-readiness” in development and SEO practices. That means: Keeping website infrastructure up-to-date (fast servers, latest security, modern frameworks optimized for SEO). Ensuring new content is published with schema and clear structure from the get-go (perhaps create templates that enforce this).

Regularly auditing robots.txt and meta directives as the search landscape changes (maybe the defaults we use today will change if, say, a major search engine decides to use a different crawler or require an explicit opt-in for AI features). Coordinating with content creators to place important info in places (or formats) that will get noticed by AI. For example, if there’s a critical statistic or quote, maybe make it a one-sentence paragraph or a call-out that an AI won’t miss.

Monitoring performance and logs – watch for any crawler issues, page speed regressions, or unusual bot activity that could indicate an AI scraping your site in undesirable ways. Embracing new standards early – being among the first to implement something like llms.txt could give an advantage if LLM-powered services start looking for it to enhance their answers. In essence, technical SEO for GEO is about being proactive and detail-oriented.

Small tweaks (like a meta tag here, an alt text there, a 0.1s load improvement) can compound into a significant edge when multiplied across thousands of queries and users. It’s akin to tuning an engine – each adjustment might only improve things slightly, but together they make your site a high-performance machine in the race for AI-age visibility.

The companies that master technical SEO in this era will find that their content reaches not only more people but the right people at the right moments – whether through a chatbot, a voice assistant, or the evolving search result pages. By following the strategies in this post, you’ll ensure the technical fidelity of your site matches the excellence of your content, creating a synergy that propels your online visibility to new heights, no matter how search evolves.