Creating separate markdown pages specifically for large language models (LLMs) has recently become a topic of discussion among SEO professionals and webmasters. However, both Google and Bing experts advise against this practice due to potential policy violations and technical inefficiencies. This article explores why serving different markdown content to LLMs instead of standard HTML to users could harm SEO rather than help.

Understanding the Concept of Separate Markdown Pages for LLMs

The idea behind creating separate markdown (.md) pages is to provide Large Language Models with a cleaner, simplified version of website content that might be easier for AI to parse. Advocates suggest that by offering a dedicated URL with markdown content, LLMs could theoretically better comprehend the text and extract relevant information, potentially benefiting AI-driven search results.

Despite these assumptions, this method involves serving different content to bots compared to human users, a practice referred to as cloaking. Cloaking has long been against Google’s webmaster guidelines because it manipulates search engines by showing content that users do not see.

Google’s Position on Separate Markdown Pages

John Mueller, a prominent Webmaster Trends Analyst at Google, has been vocal about this topic. Mueller points out that since LLMs are trained on and can parse normal HTML web pages, there is no need for webmasters to create specialized markdown pages for these models. He questions the rationale behind providing content to LLMs that differs from what users see.

“LLMs have trained on, read, and parsed normal web pages since the beginning. Why would they want to see a page that no user sees?” – John Mueller

Mueller also challenged the idea as impractical and ill-advised, emphasizing that LLMs can even understand images, thereby underlining his view that converting entire sites into markdown files is an extreme and unnecessary action.

Bing’s Perspective on the Practice

Fabrice Canel from Microsoft Bing’s search team weighed in on this topic as well. Canel expressed concern about the extra crawling burden this approach would impose. From Bing’s side, search engines are designed to crawl and verify content similarity between pages, and serving different versions could lead to neglect or broken content.

“We’ll crawl anyway to check similarity. Non-user versions are often neglected or broken. Humans’ eyes help fix both what people and bots see.” – Fabrice Canel

Bing also emphasized that using structured data such as Schema markup embedded in pages significantly aids AI in comprehending website content better, without needing separate markdown pages.

The SEO Risks of Providing Different Content for LLMs

Serving separate content creates a risk of being classified as cloaking, which violates search engine guidelines. Cloaking can lead to penalties, lowering a website’s rankings or removal from search indexes entirely. Moreover, search engines have become more sophisticated in detecting content discrepancies between what users see and what bots crawl.

Duplicate content management becomes more complex when markdown pages are introduced solely for AI indexing. This complexity can potentially hurt overall site authority and trustworthiness in search engine algorithms. As SEO expert Lily Ray highlighted, managing duplicate content and serving different content versions raises significant concerns about long-term SEO health and compliance with search engine policies.

Best Practices for Optimizing Content for AI and LLMs

Rather than attempting shortcuts by generating separate markdown pages, webmasters should focus on maintaining a single, well-structured HTML page optimized for both users and AI. Using semantic HTML, clear headings, and Schema markup reliably improves content understanding by AI without risking policy violations.

Ensuring that the content served to users and crawlers is identical builds trust with search engines and prevents penalties. Enhancing page speed, mobile-friendliness, and accessibility are also crucial factors that help AI and conventional search algorithms interpret a site effectively.

Leveraging Structured Data

Implementing Schema.org structured data provides explicit signals about the content’s context, which AI systems increasingly depend on for accurate interpretation. Structured data helps search engines better classify pages and enhances the chance of rich results in search results.

Conclusion

Creating separate markdown pages for LLMs is not a recommended SEO practice. It carries the risk of cloaking, duplicate content issues, and potentially harms a site’s search ranking. Both Google and Bing encourage webmasters to rely on standard HTML pages and structured data to make content accessible and understandable to AI, rather than using workarounds that could be penalized.

As AI-powered search technologies evolve, ethical SEO practices focusing on quality, transparency, and user experience remain the best foundation for sustainable organic growth.

Further Resources and Expert Guidance

Webmasters looking to optimize their sites for AI and evolving search engine algorithms may consult resources such as Google’s developer documentation at https://developers.google.com/search or Bing’s webmaster guidelines at https://www.bing.com/webmaster. These sources provide up-to-date recommendations on structured data, content best practices, and AI-readiness.

SEO analyst Natasha Gomez remarks,

“Adhering to core webmaster guidelines and focusing on creating authentic user-centric content ultimately benefits SEO more than any attempt to manipulate AI through backend hacks.”

Ultimately, the consensus among search engine experts is to maintain a unified content approach that is user-friendly and technically sound, avoiding experimental tactics like separate markdown pages for AI bots.