How to rank in LLMS with Jesper Nissen and Jabez Reuben

How to rank in LLMS with Jesper Nissen and Jabez Reuben:

Watch the video here: https://youtu.be/c6YLt5PZ05E

Details from the video on how to rank in Chatgpt, Claude and other LLMS

• Introduction to LLM and AI Visibility: The discussion began by addressing ranking visibility within large language models (LLMs) such as ChatGPT and Claude, and how this relates to AI overview results. Jesper Nissen shared that their parasite SEO methods, including posting on social media and other properties, can influence LLM visibility .
• Jabez Reuben's Professional Background: Jabez Reuben, based in Chiang Mai, Thailand, detailed their background in SEO, which started in an agency before they founded their own agency, The Blueprints, and developed a SaaS product called Link Validator. They also operate a conference, recently rebranded to LLM Mastery, and are developing a platform intended to be a consensus builder for LLMs .
• Rebranding the Conference to LLM Mastery: The focus of their conference, Link Building Mastery, has been rebranded to LLM Mastery to reflect their agency's shift in focus toward LLM visibility, ranking, and automation. This conference is currently scheduled to hold its first session in Chiang Mai and is targeting over 250 attendees .
• Educating Clients on LLM SEO: Jabez Reuben noted that their agency is actively educating clients about the necessity of preparing for both LLM visibility and traditional Google SEO, advising that just focusing on Google is insufficient. By balancing article publication with listicles and consensus building, they have observed sites ranking well on both LLMs and Google, sometimes having four out of five organic results being their published articles.
• Measurability of LLM SEO Results: A key benefit of LLM ranking work is the ability to pinpoint the success of specific actions, which was more difficult with regular link building or on-page SEO. When a client sees their published citation being used, the agency can clearly identify the work that made the difference.
• LLM Visibility and its Connection to Google Rankings: Jesper Nissen and Jabez Reuben discussed the connection between LLM and Google visibility, with Jabez Reuben noting that ranking in Google's top 10 or top 20 can significantly increase the chances of ranking in LLMs like Claude, Perplexity, and Gemini . However, ChatGPT's indexing is distinct, as it relies on Bing results and its own training data, which may not be regularly refreshed.
• Importance of Consensus Building for LLM Ranking: Jabez Reuben emphasized that building consensus is the most critical element for ranking in LLMs and AI overview results, even suggesting it also helps with Google rankings. Consensus building involves establishing topical authority on third-party sites about a brand, similar to how human perception of brands is formed when they are seen everywhere
• Citation Weightage in LLMs: Citing a study, Jabez Reuben noted that the majority of weightage in citations is given to search results, followed by content from sites like Reddit, which is often used for answers but not frequently cited . They confirmed that content published on third-party sites, even if not directly cited, may still be used as background information to push results for a brand .
• Quantifying the Work Required for Citation and Fan-Out Queries: Quantifying the exact number of guest posts or social media posts required for citation is difficult, but initial efforts often start with a minimum of five posts per keyword The search behavior in LLMs involves detailed searches and personalized answers, where the LLM performs multiple, varied "fan-out queries" in the background before constructing the final response .
• Strategic Content Coverage for Fan-Out Queries: To effectively cover fan-out queries for a brand (e.g., an accounting SaaS), content should be detailed and highly specific, covering variations such as "best accounting software for freelancers" or "best accounting software for dentists in Toronto" . This approach requires covering every vertical in detail, as opposed to just focusing on a broad search term .
• LLM Search Behavior Versus Old-School SEO: A major difference between LLM ranking and traditional SEO is that LLMs perform multiple, unseen fan-out queries based on a user's initial search to construct a comprehensive AI answer. This means the search experience is changing, with users spending less time navigating websites from Google and more time making decisions based on detailed LLM and Reddit answers .
• LLM Visibility and Conversion Rates: The conversion rate for products cited within LLM answers is significantly higher, estimated to be three or four times more than traditional Google traffic . This high conversion rate provides a strong incentive for businesses to focus on obtaining visibility in LLM results-
• Terminology of Search Optimization: Jabez Reuben expressed a preference for simple terms like "search optimization," "ranking optimization," or "visibility optimization," arguing that the focus should remain on strategies that ultimately bring more business, rather than on new acronyms like Generative Engine Optimization (GEO) . The core work resembles traditional SEO, but with a greater focus on the type of content posted .
• The Effectiveness of Listicles and Topical Authority: Listicles, or roundup articles (e.g., "Top 20 protein powders"), are highly effective for LLM ranking because they enable faster information retrieval . To compete in tougher niches, one must publish not only listicles but also comparison articles, reviews, buying guides, user guides, and even articles about brands to avoid, which are referred to as antonyms .
• Legal Considerations in Publishing Listicles: To comply with guidelines, such as those from the FTC, one should avoid claiming that every product has been tested . Instead, articles should be presented as a survey, poll, or analysis, including a disclaimer stating that the content reflects the editor's point of view, which helps brands cover themselves legally .
• Leveraging Digital Real Estate for Visibility: Jabez Reuben advised against wasting any digital real estate and recommends leveraging all available platforms, including Reddit, YouTube, Web 2.0 sites (like Medium and LinkedIn), and cloud sites, to publish content coverage. They also suggested a tiered approach where less expensive guest post sites feature links to published listicles, while mid-tier guest post sites include links to the client's main site .
• Unlinked Brand Mentions and Article Structure: Unlinked branded mentions, where a brand is mentioned without a direct link to their site, are beneficial for LLM rankings . However, merely mentioning a brand is insufficient; the entire article must be well-structured, media-rich, and built around the brand's name to effectively push the brand's ranking .
• AI Overview Ranking Strategy: Ranking in Google's AI overview is often the fastest outcome, especially for more detailed, long-tail queries. For non-Your Money or Your Life (YMYL) niches, it is possible to quickly "flip" the AI overview to cite new content in just minutes . For YMYL niches, however, even if an article ranks number one organically, it may not be easily picked up in the AI overview citations due to stricter rules .
• Ranking Speed and Bottom-Up Approach: The speed of ranking in LLMs varies significantly based on the niche and competitor work, with the fastest observed result being 12 hours for a query in one case study . A recommended strategy is the bottom-up approach, where one starts by ranking for sub-subcategories (detailed fan-out queries) and gradually builds up authority to rank for broader keywords .

Identifying Fan-Out Queries: Fan-out queries can be identified using tools like the Ahrefs Brand Radar, or by using technical methods within ChatGPT by inspecting the browser after a search . Jabez Reuben strongly recommends incorporating these detailed fan-out queries directly into articles to optimize for LLM visibility .