As large language models (LLMs) like ChatGPT, Perplexity, Claude, and Grok become increasingly central to how people search for and consume information, a new dimension of search engine optimization is emerging: LLM SEO. Unlike traditional SEO, which focuses primarily on ranking within search engines like Google or Bing, LLM SEO aims to make content visible and retrievable by AI systems that generate natural-language answers.
LLMs pull their responses from a variety of web sources, and while the exact ranking mechanisms remain opaque, early research and experimentation reveal patterns: language models tend to favor content that is structured, frequently updated, semantically clear, and widely referenced. For businesses, marketers, publishers, and content creators, optimizing for AI visibility is rapidly becoming as important as classic SEO techniques.
Here are eight strategic practices to improve content visibility within LLM-driven platforms.
1. Connect to Bing Webmaster Tools
Although Google remains the dominant search engine globally, Microsoft Bing plays a central role in the LLM ecosystem. For example, ChatGPT with web browsing enabled relies heavily on Bing’s index. Submitting a website’s XML sitemap to Bing via Bing Webmaster Tools increases the likelihood of content being crawled and included in Microsoft’s search infrastructure.
By verifying domain ownership and actively managing crawling preferences through this tool, website owners ensure that their latest content is visible to Bing, and, by extension, to any AI products that rely on its data.
2. Implement Schema Markup
Search engines and language models rely on semantic signals to understand the meaning of web content. Adding structured data through schema markup (such as JSON-LD) helps define elements like product descriptions, FAQs, authorship, publication dates, and more. This not only enhances visibility in search engine results but also makes it easier for LLMs to parse and quote relevant sections in their answers.
Pages that feature schema markup are more likely to appear in AI-generated responses, especially in contexts like direct questions, comparisons, or definitions. Tools like Google’s Structured Data Testing Tool or Schema.org can assist in implementation.
3. Apply Traditional SEO Best Practices
LLMs are trained on the open web, which includes data indexed by Google, Bing, and other search engines. Therefore, content that performs well in traditional search rankings tends to also perform well in LLM-generated answers. This means standard SEO techniques—on-page keyword optimization, internal linking, user-friendly design, and descriptive meta tags—remain highly relevant.
Additionally, ensuring that content is mobile-friendly, fast-loading, and secured with HTTPS can indirectly affect its credibility and exposure in AI-generated outputs.
4. Structure Content for Natural Language Queries
One defining characteristic of AI search is its reliance on natural language inputs. Instead of typing “SEO checklist 2025,” users might ask, “What are the most important SEO tasks for 2025?” To align with this shift, content should be optimized around question-answer formats.
Incorporating FAQ sections, using H2 or H3 subheadings phrased as full questions, and clearly answering them in concise paragraphs helps LLMs extract and reproduce relevant snippets. Analyzing autocomplete results from tools like Google Search or ChatGPT itself can guide the creation of these question-answer pairs.
5. Keep Content Fresh and Up to Date
Timeliness is a strong relevancy signal for AI models. LLMs tend to favor recently updated sources, especially in fast-moving fields like technology, finance, or digital marketing. Even small changes—such as updating statistics, rewording outdated phrasing, or adding new references—can prompt crawlers to reassess the content.
It is recommended to audit and refresh evergreen content every 3–6 months, and to update the “last modified” date accordingly. Doing so helps maintain authority and visibility across both traditional and AI-powered discovery tools.
6. Prioritize Original, Human-Written Content
AI-generated content is increasingly prevalent—but LLMs are generally trained to avoid referencing other AI-generated sources. They prioritize original content that demonstrates expertise, experience, and human perspective. Copy-pasted or automatically generated articles are often excluded from trusted datasets.
To maximize visibility in AI responses, content should be original, insightful, and ideally backed by first-hand experience or proprietary data. Including quotes, personal analysis, or unique case studies signals higher value to both human readers and LLMs.
7. Secure Mentions on Reputable External Websites
Unlike traditional SEO, which places heavy emphasis on backlinks, LLMs are capable of drawing context from mentions—whether or not they are hyperlinked. Being referenced by authoritative websites, industry blogs, or trusted news sources helps a brand or domain become associated with expertise in a given niche.
Building relationships with journalists, contributing guest posts, participating in expert roundups, or being quoted in research reports can all lead to unlinked brand mentions. These contribute to the semantic reputation of a brand or domain in the broader web ecosystem.
8. Increase Branded Search Demand
A brand that is frequently searched by name sends strong trust signals to both search engines and language models. AI systems take into account not only content quality, but also public interest. Branded search traffic—such as queries including a company’s name or product—suggests that the brand is relevant and trustworthy.
To increase branded search volume, businesses can invest in social media campaigns, partnerships with influencers, newsletter marketing, and public relations. Awareness-building efforts outside of SEO still feed back into AI models that track and interpret web-wide attention trends.
Conclusion: The Future of SEO Includes AI Optimization
As generative AI becomes a standard part of the way users search and discover information, content visibility within LLMs will become just as critical as visibility in Google or Bing. By combining classic SEO fundamentals with AI-specific strategies—such as structured data, Q&A formatting, and regular updates—brands can stay competitive in both environments.
Rather than replacing traditional SEO, LLM SEO builds on it, extending optimization efforts into the emerging space of AI-powered discovery. The earlier these techniques are adopted, the greater the long-term advantage in a world where language models shape how knowledge is accessed.
