What Are the Best Practices for Optimizing Content for Generative AI?

The best practices for optimizing content for generative AI are to structure articles with clear, search-style headings and lists, answer questions directly and concisely, and focus on guide-like helpful content instead of promotion. Adding schema markup, aligning with conversational queries, and building authority across trusted sources make your content easier for AI to reuse. To stay visible, track your brand’s share of voice in AI answers and update content regularly with new data and context.

Generative Engine Optimization (GEO) is the new layer of SEO. Unlike traditional search, AI platforms such as ChatGPT, Perplexity, and Google Gemini deliver direct answers instead of lists of links. If your brand isn’t included in those answers, you risk invisibility with millions of daily users.

This article will show you how to:

• Make your content AI-friendly with structure and clarity
  
  
• Use schema markup and metadata to improve visibility
  
  
• Align content with conversational queries
  
  
• Build trust and authority across sources AI models respect
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• Track and improve your AI share of voice
  
  

By the end, you’ll have a practical playbook for ensuring your brand is not only optimized for Google, but also discoverable in AI-powered search, the channel that will dominate the future of visibility.

What kinds of content structures do AI assistants prefer?

AI assistants favor content that is structured with clear headings, short paragraphs, and lists they can easily extract. Well-formatted, factual, and conversational content has a much higher chance of being cited in AI-generated answers.

AI models digest content in chunks of 200–500 words, looking for concise and well-structured information. A 2025 Superlines study of 1.5 million AI citations found that 85–97% of sources came from smaller sites such as company blogs and product documentation, showing that even niche content gets visibility if it is structured clearly.

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Best practices:

• Use descriptive headings, bullet points, and short paragraphs. AI systems often lift bulleted tips or step-by-step guides verbatim. For example, if you publish an article on “Email Marketing Tips” with a bulleted list, an AI is far more likely to reuse that list than a long narrative paragraph.
  
  
• Write in a natural, conversational style. Avoid jargon or robotic phrasing. For instance, instead of writing “The implementation of complex GEO methodologies is imperative for content visibility,” write “Generative Engine Optimization (GEO) is about making your brand discoverable in AI-generated answers.” This phrasing is clear, direct, and mirrors how people actually ask questions. (Read more in our Generative Engine Optimization guide).
  
  
• Directly answer common questions. Use Q&A style headings (e.g., “What is Generative Engine Optimization?”) and support them with FAQ schema so AI can parse them. This makes your content look like ready-made snippets for AI.
  
  
• Keep it concise and evidence-based. Stick to one idea per paragraph, surface key facts clearly, and back claims with stats or expert quotes. AI is more likely to quote a factual sentence with data than a vague statement.
  
  
• Test your content with AI platforms. Prompt ChatGPT, Gemini, or Perplexity with target questions and see if they cite your content. If not, revise the phrasing to make the answer more explicit.
  
  

Pro tip: Confirm AI crawlers like OpenAI’s GPTBot are allowed in robots.txt. If blocked, your content may never be accessed for training or retrieval.

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How does schema markup improve AI visibility?

Schema markup improves AI visibility by making your content machine-readable. It helps AI assistants understand context, attribute information to your brand, and extract Q&A or step-by-step instructions directly for answers.

Schema is structured data that acts like a roadmap for machines. Instead of just reading words, AI systems use schema to confirm context, such as whether a section is a question, a step in a process, or information about your brand. The clearer the signals, the more likely your content is to be reused in AI-generated answers. Nobody knows exactly how large a role schema plays in AI Search today, but since it has been proven effective in SEO for years, it makes sense to include it. At minimum, it adds extra context for AI crawlers and it never hurts your visibility to have it in place.

Best practices:

• Implement FAQ, How-To, and Organization schema. FAQ schema makes Q&A sections machine-readable, How-To schema highlights step-based guides, and Organization schema ensures brand details (name, logo, etc.) are correctly attributed when AI cites you.
  
  
• Use JSON-LD and keep metadata fresh. JSON-LD is the recommended format. Update article titles, dates, and Open Graph tags regularly. Adding an “Article updated” field signals freshness, which AI prioritizes when citing content.
  
  
• Pull schema data directly from your CMS. If your CMS supports it, make fields like author, publish date, update date, summary, key takeaways, and FAQ part of the template. This way, the information is always exported into the schema automatically and stays up to date every time you press “publish.”
  
  
• Feed the Knowledge Graph. Ensure your brand information is consistent across Wikipedia, Wikidata, and your own About pages. Add sameAs links in your schema to connect your site to trusted external profiles (LinkedIn, Crunchbase, etc.), reducing ambiguity for AI.
  
  
• Cross-link schema with authority content. If you have cornerstone articles (for example, Generative Engine Optimization), use schema and internal linking to strengthen how AI understands your topical authority.
  
  

Pro tip: Run a quarterly check to confirm that your schema fields are still being exported correctly and that the information is up to date. CMS updates or plugin changes can silently break schema output, which means your structured data might disappear without you noticing.

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How do I optimize content for conversational queries?

To optimize content for conversational queries, write your headings and answers the way people actually ask questions in AI assistants. Focus on long-tail, natural phrases, provide complete answers that stand on their own, and expand with related subtopics for context.

AI queries are not just longer versions of Google searches, they are conversations. Instead of typing “running shoes,” users ask full, layered questions such as “What are the best running shoes for flat-footed people who run on pavement?” AI then refines the search further by asking follow-ups, creating a two-way interaction. Similarly, someone might start with “What’s a good vacation spot for someone who likes to hike but hates hot weather?” and then add details when prompted by the AI. This means queries are not only longer but also more specific, detailed, and intent-driven.

For marketers, this shift requires optimizing for intent and context, not just keywords. AI Search rewards content that mirrors how people speak, explains the “why” behind their questions, and anticipates follow-ups with connected subtopics.

Best practices:

• Target long-tail, natural queries. Use headings that reflect real phrasing, such as “How can small businesses use generative AI in marketing?” Tools like Superlines can reveal which conversational queries already appear in AI answers.
  
  
• Provide complete, context-rich answers. When the query is “How to optimize a website for AI Search?”, cover the entire process with clear steps. AI prefers comprehensive, stand-alone answers.
  
  
• Incorporate related semantic topics. Expand coverage with subtopics like personalization, A/B testing, or pitfalls. This signals topical depth and makes your content more reusable across different but related queries.
  
  
• Optimize for one-paragraph summaries. Start sections with a 2–3 sentence TL;DR answer. AI assistants and Google AI Overviews often pull these directly.
  
  
• Link related articles into clusters. Internal linking between conversational topics builds authority and signals expertise to AI.
  
  

Pro tip: Watch for overlap between voice search and AI queries. Many prompts in ChatGPT, Gemini, or Perplexity mirror how people phrase questions to Siri or Alexa. If your content works for natural voice queries, it is also optimized for AI assistants.

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Where does AI actually get its data

AI assistants pull information from a wide range of sources, not just big media outlets. In fact, most citations come from smaller sites such as company blogs, product documentation, niche communities, and Q&A platforms.

In August 2025, Superlines analyzed 1.5 million AI citations across ChatGPT, Microsoft Copilot, Google Gemini, Grok, and Perplexity. The results showed that 85–97% of citations came from smaller sources rather than large news sites. Each AI platform also had unique preferences in what it cited. Even a single mention in a niche blog, product forum, or community site can dramatically increase a brand’s visibility in AI answers.

Key takeaways:

• Fix your site first. Ensure AI can access your content (don’t block crawlers) and structure it so answers are easy to extract.
  
  
• Match conversations, not just keywords. Align your content with how your audience phrases questions and searches online.
  
  
• Expand into external sources. Work on being mentioned in the blogs, communities, and reference materials that AI models rely on.
  
  
• Measure share of voice. The most important long-term metric will be your brand’s share of voice in AI-generated answers, since visibility increasingly happens inside AI chats rather than through clicks.
  
  

Understanding where AI gets its data is only half the story. The next step is to see why AI systems favor trusted sources and how you can build authority across the channels they respect most.

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Why do AI systems favor trusted sources?

AI systems favor trusted sources because authority is based on your entire digital footprint, not just your site. Content that appears on reputable websites, communities, and knowledge bases is more likely to be cited in AI-generated answers.

Authority in the AI era goes beyond traditional SEO metrics. AI assistants cross-check information against multiple sources, looking for signals of expertise, consistency, and reliability. For example, OpenAI has partnered with Reddit to access real-time, structured content, showing that forum posts and community conversations are a valuable part of training data. This means it is important to monitor discussions on community-driven platforms and look for opportunities to gain brand visibility there, in addition to optimizing your own site.

Best practices to build authority across trusted sources:

• Get mentioned on high-authority websites. PR and guest articles in respected publications (industry blogs, news outlets, or even niche sites) boost credibility. A single expert quote in Forbes or a well-known blog can help AI models recognize you as an authority. Local or niche coverage also matters, since AIs often cite smaller publications.
  
  
• Be active on Q&A and community platforms. Sites like Quora, Reddit, and Stack Overflow are heavily referenced by AI models. Providing thoughtful, helpful answers (with your name attached) increases the chance of being picked up. Many AIs were trained on forum text, meaning your past contributions may already influence current models.
  
  
• Secure and maintain a Wikipedia presence. Wikipedia and Wikidata are go-to references for AI. Ensure company or leadership pages are accurate, well-cited, and updated. Never spam. Instead, build enough press and citations to warrant inclusion.
  
  
  
• Engage on professional networks. High-quality LinkedIn posts, thought-leadership articles, or widely shared videos expand your digital footprint. Even if AI does not directly quote a tweet or post, consistent visibility strengthens brand recognition across platforms.
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• References in research papers, industry studies, or data-driven content carry extra weight. When you are cited as an expert, AI assistants are more likely to reuse that knowledge in responses. This extends the principle of E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) into the AI realm.Much like we have done as well in the real PR example below:

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As I put it in HS Visio (July 9, 2025):

PR will pay a bigger role in the AI era, since many AI systems pull recommendations from both social media and traditional media.” — Jere Meriluoto, Co-Founder & CEO of Superlines

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Pro tip: Regularly check where competitors appear in AI answers. For example, ask “What is the best [your product category]?” in ChatGPT or Perplexity. If competitors show up and you do not, analyze which sources they are leveraging such as Wikipedia, review sites, industry blogs, or their own content, and close the gap by targeting the same opportunities.

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How do I measure AI search visibility?

Measuring AI Search visibility means tracking when, where, and how your brand is mentioned in AI-generated answers. You can do this manually to some extent by testing queries yourself, or use monitoring tools that specialize in AI Search visibility. Combining these insights with traffic analytics gives the clearest picture of your performance.

Unlike traditional SEO, where keyword rankings are easy to check, AI Search visibility is more complex. AI models generate answers dynamically, so visibility depends on whether your brand is cited or referenced in those conversations. To measure effectively:

• Check your current visibility manually. Start by asking common customer-style questions in ChatGPT, Perplexity, Gemini, or Claude. Note whether your brand appears, what information is shown, and if it is accurate. For example, a CRM provider might test queries like “What’s the best CRM for small businesses?” and see if they are mentioned.
  
  
• Use monitoring tools to scale. New solutions now automate this process by tracking brand mentions across AI platforms and surfacing the exact sources behind them. This helps you compare your visibility against competitors and identify content or authority gaps.
  
  
• Combine traffic and share of voice. Analytics tools such as GA4 can capture referral traffic from AI platforms when users click citations. Because many AI answers are “zero-click,” it is equally important to track your brand’s share of voice (SoV), how often you are mentioned even without a click. For instance, a recent study found that when users saw an AI summary in Google search, only 8% of visits led to a click to a standard search result link. When there was no summary, that share was ~15%.
  
  
• Iterate based on insights. If certain articles are consistently cited, analyze their structure and style, then replicate those practices across other content. If you are absent from key queries, study the sources AI is citing and adjust your strategy to fill the gap. Stay agile as AI platforms evolve.
  
  

Pro Tip:

Do not just track whether your brand is mentioned in AI answers, check how it is being described. Outdated or misleading information can hurt more than invisibility. Even “zero-click” exposure still builds recognition and trust, so treat both accurate mentions and referral traffic as success metrics in your AI Search strategy.

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To summarize the learnings of the best practices:
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Success in AI Search comes down to being where the conversations happen, making your content clear, verifiable, and present across the sources AI trusts most.

Improving AI Search visibility takes consistent tracking, content alignment, and presence in the sources AI assistants trust. If you want to explore your current visibility or exchange thoughts on this topic, feel free to contact us or connect with me on LinkedIn. I am always happy to discuss.

— Jere Meriluoto, CEO & Co-Founder of Superlines