Optimizing content for AI

Context

As AI-powered discovery tools reshape how users search, brands must evolve their content strategies to stay visible, relevant and competitive. As foundational SEO best practices are still key, emerging techniques are required tailored to how large language models and AI bots retrieve and interpret content.

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Execution

The fundamentals are the same: content must be valuable, structured, and relevant. The goal is to be understood, selected and reused by AI models across an expanding range of formats and platforms. That means structuring content for machine readability and measuring performance through a new lens that includes metrics like AI visibility and citation frequency as well as prompt alignment.

The fundamentals SEO playbook focused on 4 key areas:

1. Keyword Optimization - Publish content around target terms and phrases to rank in search results. This was how brands aligned themselves directly with user intent.
2. Backlinks & Authority - Earn or build inbound links from reputable sites to signal trust and relevance. Backlinks became one of the strongest signals in Google’s ranking algorithm.
3. Website Performance - Optimize for speed, mobile responsiveness, and accessibility to improve ranking and user experience. Core Web Vitals further cemented performance as a critical SEO factor.
4. E-A-T / E-E-A-T - Demonstrate expertise, authority, and trustworthiness with “experience” later added as a key factor. These signals reassured both users and search engines of credibility. Read more on Google Search Central.

If you followed this formula, Google rewarded you with traffic. But as AI is now rewriting the rules of discovery now comes down to how well your content can be understood, reused, and cited by AI systems.

Introducing Generative Engine Optimization (GEO)

GEO is about making sure your content is structured and clear enough to be picked up by AI engines, reused in their answers, and ideally cited as a source. Unlike SEO, which is about signaling relevance to a ranking algorithm, GEO is about signaling usability to a AI model. It recognizes that people are increasingly getting their answers directly from AI, often without ever clicking through to a website.

Best practices for AI-Ready Content

SEO ensures presence in traditional search, GEO ensures presence in generative answers. While Google remains dominant with ~90% share and 5 trillion queries per year. ChatGPT users actually increase Google searches (10.5 → 12.6 sessions/week) while adding 5 ChatGPT sessions/week on top. Traditional search is holding steady, while AI-first discovery is expanding rapidly.

This checklist is designed to start guiding you to evaluate and optimize your content and operations, and to uncover gaps where you should be adding add real value. Want to deep dive in more detail? More information is available in the Insights section.

Insights

As AI platforms like ChatGPT, Gemini, and Perplexity reshape discovery, content must go beyond traditional SEO to become AI-ready structured, crawlable, and credible enough to be reused in generative answers. This means ensuring technical accessibility (crawlability, indexability), structuring content for chunk-based retrieval, building topical authority through hubs and clusters, reinforcing trust with fresh and cited sources, optimizing your own site’s search experience, and continuously measuring and refining performance. Together, these practices form the foundation of Generative Engine Optimization (GEO), ensuring visibility not only in search rankings but also in AI-driven answers.

1. Crawlability and indexability

If your content isn’t crawlable, it won’t be visible in AI answers. Just like traditional SEO, a noindex or robots.txt block will keep pages out of AI Overviews, Gemini, or ChatGPT results. What’s different in AI discovery is how much it leans on Google’s index. So strong SEO visibility directly increases your chances of being reused in AI answers.

But AI crawlers are not Googlebot. Google can render JavaScript-heavy pages, but most AI crawlers don’t. They rely on plain HTML. That means if your content only loads client-side, AI engines might never see it. So it’s key to have the basics covered:

• Content blocked by noindex, robots.txt, or x-robots headers won’t appear in AI Overviews, Gemini, or ChatGPT results.
• Studies show ChatGPT citations often mirror Google results. Strong SEO visibility increases your chances of being reused in AI answers.

Optimizing your XM Cloud setup, keep in mind:

• Use SSR & Pre-rendering: Next.js in XM Cloud supports SSG, SSR, and ISR, ensuring bots that don’t run JavaScript can index fully rendered HTML. Reduces latency, ensuring AI bots don’t time out during crawl.
• Metadata controls: Make sure marketers can manage titles, meta descriptions, canonical tags, and robot instructions per page without dev intervention as long as these are populated. These can be populated directly in the pages with dynamic metadata available in Next.js.
• Localization: XM Cloud manages languages and prevents thin content duplication, ensuring AI engines interpret regional content correctly. Refer to the Multilingual content and localization recipe for more information.

2. Crawlability and indexability

AI doesn’t look at your pages the way we do, it retrieves content in chunks. That means each section of your site needs to stand on its own. Clear headings, FAQs, and short answer-led paragraphs increase your chances of being pulled into AI answers. Schema types like FAQPage, HowTo, and Product give search engines and AI tools stronger signals about how to reuse your content.

Component-based authoring and schema-driven templates mean FAQs, summaries, and metadata are stored in structured fields, not buried in rich text. Add to that reusable content blocks and you’ve got a system where content is modular, consistent, and AI-ready by design.

3. Topical Authority

Topical authority is one of the strongest signals for both search and AI discovery. AI engines assemble answers from multiple angles so sites with deep content hubs around a subject are far more likely to be reused. That means building pillar and cluster structures, where a cornerstone page is supported by interlinked detail pages. Keeping these updated regularly signals freshness and authority, and internal linking reinforces those relationships.

The Pillar and Cluster Model

A tried-and-true method for building topical authority is the pillar + cluster structure. At the center, you create cornerstone content that addresses a core topic comprehensively. From there, supporting cluster pages dive deeper into subtopics, answering specific questions and filling out the knowledge base. The combination works because it balances big-picture coverage with granular detail, offering both context and depth.

Staying Fresh Signals Authority

One of the biggest indicators of authority is freshness. Cornerstone content that’s regularly updated with the latest data, insights, and context doesn’t just serve readers better, it also shows search engines and AI crawlers that your expertise is current. Frequent updates help your site maintain relevance and improve its chances of being chosen as a source.

Strength in Internal Linking

Authority isn’t only about what you say but also about how you connect ideas. Internal linking strengthens relationships between your content. When you use context-rich anchor text to tie related pieces together, you guide both users and search engines through your knowledge ecosystem. This interconnectedness reinforces authority in ways that isolated articles never could.

For organizations managing large-scale content, Sitecore XM Cloud provide specific advantages for structuring content hubs:

• Taxonomy & tagging: You can model clusters by topic, industry, or persona, ensuring that your hubs are organized and structured for discovery.
• API-based requests: Entire content clusters can be surfaced programmatically in AI-friendly formats, making it easier for engines to consume your content.
• Analytics: With XM Cloud & Sitecore Search in place, use your built-in analytics to see what users are searching for, spot content gaps, and create new clusters to meet demand. Comparing this to your analytics should show you the gap of what users are ultimately search or prompting AI for.

4. Authority, Citations & Personalization Resilience

Authority and trust are just as critical in GEO as they are in SEO. AI doesn’t cite everything it uses, it cites what it sees as trustworthy. EEAT still applies: expertise, experience, authority, and trust. Freshness is even more amplified in AI. Studies show LLMs tend to cite content that’s significantly newer than what Google surfaces. Proof points like case studies, certifications, and data-backed claims further reinforce authority.

To strengthen these strategies, and scale them effectively, make sure that while setting up XM Cloud:

• Author profiles as structured fields: Model bios, credentials, and bylines consistently to reinforce expertise. Consider that these might not just be against your usual ‘article’ pages but also other types of content.

• Reusable testimonial & endorsement content: Surface certifications, social proof, and validation across all digital experiences. Testimonials, case studies, certifications, and data-backed claims provide validation that your content can be trusted. Make sure you have a content library authors can reference.

• Variants & personalization rules: Deliver persona- or region-specific messaging in real time. AI models consider these variants so make sure they are properly in place.

• Localization support: Ensure proper hreflang tagging and translation governance to prevent thin or duplicate content. Regional content with personalization, hreflang tagging, and local examples ensure AI delivers content in ways that resonate across geographies and personas.

• Search indexing: Sitecore Search indexes not only content, but also bios, endorsements, and localized variants, making them more discoverable and reusable by AI engines. This makes sure your site search is also considering the same content, and you are able to tweak your search accordingly.

5. Website search

As AI-driven search engines reshape how people find and consume information, it’s easy to focus exclusively on Google, ChatGPT, or Perplexity. But there’s another search experience that’s just as critical: your own website search.

For many visitors, the internal search bar is the first stop after landing on your site. If it doesn’t deliver meaningful, contextual results, you’re missing opportunities to engage, convert, and prove authority.

• Move beyond keyword matching - Users expect search to “understand” them. Use semantic search which interprets meaning, not just keywords, allowing your website to return results that actually answer the user’s intent.

• Index the right content - A powerful search engine is only as good as the content it can access. That means going beyond blog posts and landing pages to include Documentation, FAQs, Metadata for gated content (like PDFs or whitepapers). Even if the full asset isn’t available publicly, indexing its metadata ensures users know it exists and can be guided to the right next step. This approach expands the search surface area while respecting access controls.

• Apply Taxonomies for relevance - Content taxonomies, whether organized by topics, personas, or industries, play a huge role in delivering relevant search results. By tagging and structuring content properly, you help your search engine make smarter matches and ensure users find information tailored to their needs.

• Use Insights to fill gaps - Internal search is a goldmine of intent data. By analyzing what users are searching for, and whether they’re finding it, you can uncover content gaps and even identify new SEO opportunities. If your audience is consistently searching for answers you don’t provide, that’s your cue to create or optimize content.

• Contextualize Results - Not all users are the same. Adapt results based on Location (delivering region-specific answers or offers), Persona ( tailoring results for developers vs. marketers) or Behavior (prioritizing content based on what users have engaged with before). This ensures every search result feels relevant.

• Make Search a conversion tool - Search doesn’t just need to provide answers, it can also guide users toward action. By integrating CTAs, related content, or suggested actions directly into results pages, you turn a basic search into a dynamic engagement hub. Instead of dead ends, users find pathways forward.

6. Optimization & Measurement

Optimization and measurement in GEO are about visibility and continuous improvement. AI referrals can be tracked: ChatGPT and others often send traffic with identifiable tags like utm_source=chatgpt, which you can segment in GA4 to distinguish it from Google. Bot activity is also visible in CDN or server logs, like Cloudflare, where you’ll see OAI Search Bot, PerplexityBot, and others. And finally, experimentation matters. Different formats and structures perform differently in AI.

• Experimentation with A/B/n Testing: AI doesn’t always favor content in predictable ways. With XM Cloud’s A/B/n testing, teams can run experiments directly inside the platform—testing multiple variants of the same content, including AI-optimized phrasing. This helps uncover which formats, tones, and structures perform best when surfaced by AI engines.

• Smarter Personalization Rules: AI is context-driven, which makes content adaptability critical. XM Cloud enables personalization rules that deliver simplified, audience-specific content in real time. By streamlining content for different personas and contexts, brands increase the likelihood of their material being reused by AI platforms across multiple scenarios.

• Workflow Governance for Content quality: Optimization is only as strong as the governance behind it. XM Cloud provides workflow governance tools that ensure high-value content is regularly updated, reviewed, and approved. This minimizes the risk of outdated or inconsistent material making its way into AI answers, while keeping authority signals intact.

• Search Analytics for Continuous Refinement: XM Cloud page analytics and Sitecore Search analytics, giving teams insights into the queries, intents, and conversions that matter most. By analyzing how users interact with content, and how AI platforms surface it, marketers can refine their clusters and continuously improve visibility in both search and AI-driven experiences.

Related Recipes

Related Documentation