Search is undergoing a major shift as users turn to Al chatbots and large language models (LLMs) (like ChatGPT, Gemini, Bing Chat) to answer questions that used to be typed into Google. For example, instead of Browse a list of links for "best Italian restaurant in Hong Kong," users can ask an Al assistant and get a direct answer. This paradigm means businesses must adapt their SEO strategies to ensure they are mentioned and recommended by LLMs in these conversational results. The popularity of Al search is skyrocketing - ChatGPT's website now attracts billions of visits monthly (nearing ~5% of Google's traffic) . Being featured in Al-generated answers can greatly boost a brand's visibility and credibility. Below, we explore how LLMs select and rank results (generally and for restaurants), what ranking criteria they use (with relative importance), how Al-driven search differs from traditional SEO, and actionable tips to optimize your content for this new search landscape. How LLMs Select and Rank Results (e.g. "Best Italian Restaurant in Hong Kong")

LLM-based search engines aim to provide a single, comprehensive answer rather than a list of websites. When a user asks something like "best Italian restaurant in Hong Kong," an LLM will parse the query and attempt to name a few top restaurants with a brief description instead of showing 10 blue links. How does the Al decide which restaurants to mention? It works by synthesizing information from its training data or from live search results (if the LLM has Browse capability) to identify establishments that consistently appear as top-rated or "best" in trustworthy sources:

• Reliance on Training Data & Web Content: Models like ChatGPT (without live Browse) generate answers based on patterns in their training data. They will recommend restaurants that were frequently written about as the "best" in Hong Kong in articles, reviews, or lists the model ingested. In practice, this means an LLM's answer often mirrors the consensus of numerous web sources - e.g. if multiple travel guides and food blogs all praise Restaurant X as a top Italian spot, the Al is likely to include Restaurant X in its answer. In one analysis, many of ChatGPT's business recommendations were pulled from sites that publish "best X" listicles (often affiliate or review sites) , indicating that widely-mentioned names in "top" lists heavily influence the Al's choices. Essentially, the more a restaurant is talked about positively across the web, the more likely a chatbot will consider it among the "best."

• Real-Time Retrieval (Bing Chat, Gemini): Some LLMs (like Bing's Al chat or Google's Gemini) actually perform a web search and then have the LLM compose an answer. For our example query, the Al might query Bing/Google for "best Italian restaurants Hong Kong" and retrieve the top results (such as TripAdvisor rankings, magazine articles, or food blogs). The LLM will read those pages and consolidate the overlapping recommendations into its response. Restaurants that appear across multiple top search results (for example, a restaurant that is featured in several "Top 10 Italian Restaurants in Hong Kong" articles and has stellar reviews) are very likely to be named in the Al's answer. In essence, the LLM is ranking by consensus: if a name shows up repeatedly in highly-ranked content, that's a strong signal. Studies confirm a strong correlation between traditional search rankings and LLM results - brands that rank on page 1 of Google/Bing for a given topic tend to get mentioned by LLM answers for that topic. So, SEO and Al recommendations go hand-in-hand: performing well in search increases the odds an LLM will find and mention you.

• User Satisfaction Signals: LLMs also factor in qualitative signals of quality and reputation. For a restaurant, this means customer reviews and ratings are influential. An Al model aims to suggest options that will satisfy the user, so it prefers businesses with lots of positive reviews and accolades in the data it has seen. For example, if Restaurant Y has hundreds of 5-star reviews on Google, Tripadvisor, and OpenRice (a Hong Kong dining platform), and those facts are reflected in web content, an Al will interpret Restaurant Y as a highly regarded option. In fact, ChatGPT explicitly noted that it considers a business's reputation and track record when making recommendations . High ratings and frequent mentions on trusted review platforms signal that a restaurant is well-regarded, which boosts its chances of appearing in an Al-driven "best of" answer .

It's important to remember that today's LLMs don't have an intrinsic, updated database of every restaurant and its real-time ratings - they rely on what information they've been trained on or can fetch. Thus, they approximate a ranking based on the frequency, recency, and authority of mentions about each candidate. In practice, an LLM's answer for "best Italian restaurant in Hong Kong" will likely name a handful of restaurants that: (a) are clearly Italian and in Hong Kong (relevance), (b) have been highlighted by multiple reputable sources (travel guides, food bloggers, news sites, etc.), and (c) have a strong reputation (awards, positive reviews, longevity). For instance, if three different food magazines and TripAdvisor all list II Primo as a top Italian eatery, and it has a 4.8 $/5$ rating from diners, those signals collectively push the Al to include II Primo in its answer. On the other hand, a brand-new Italian restaurant that has little web presence or a place with mixed reviews is unlikely to be chosen by the Al as "the best" due to lack of supporting data. In short, LLMs rank answers by looking for widely endorsed, authoritative, and relevant information in their corpus, rather than by using the traditional search engine algorithms. This new approach has its quirks - Neil Patel's experiment found that ChatGPT's recommendations weren't always spot-on (about 27% of responses were inaccurate or off-base) - but the overall pattern is that the businesses that dominate the online conversation tend to rise to the top in LLM-generated results.

Key Ranking Criteria LLMs Rely On (and Their Importance) #

What specific factors does an LLM seem to use when deciding which brands or businesses to recommend? Recent research analyzing ChatGPT's answers uncovered six key factors that strongly correlate with whether a product, service, or brand gets recommended by the Al . These factors closely mirror the elements that make a business prominent and trusted online. While LLMs don't have official "ranking factor weights" like Google's known algorithm factors, this analysis by SEO experts provides a good approximation of each factor's relative influence. Below is a chart from an NP Digital study (by Neil Patel's team) illustrating these six factors and their relative strength (higher bars indicate a stronger correlation with being recommended by ChatGPT):

Six major factors that affect whether ChatGPT will recommend a product, service, or brand in its answer. Higher "scores" (shown above each bar, on a 0 to 1 scale) mean a stronger influence. Notably, Relevancy to the query and Brand Mentions across the web are among the highest-weighted factors, while being featured in third-party "recommendation" lists, though beneficial, is a weaker factor by comparison . From this analysis, we can derive the following LLM ranking criteria, roughly in order of importance:

• Relevancy: Is the content about your business relevant to the specific query? This was identified as the most influential factor, with a very high correlation (~0.91) . In essence, the Al checks if the keywords and topic of the user's question appear in context with your brand across the web. For example, if the query is "best Italian restaurant in Hong Kong," an LLM will favor restaurants that are explicitly mentioned in connection with "best Italian" and "Hong Kong" on various websites. One proxy for relevancy is whether your site (or content about your business) ranks in traditional search for those keywords. If you have content that targets the query (e.g. blog posts or descriptions that include phrases like "best Italian restaurant in Hong Kong") or if others have written about you using those terms, it greatly improves relevancy. Essentially, the Al needs to see a clear topical match - your brand should be consistently talked about in the context of whatever the user is asking.

• Brand Mentions: How frequently is your brand name talked about on other websites? This is a measure of online popularity and awareness, and it was nearly as important as relevancy (correlation ~0.87) . The more often your restaurant or business is mentioned across the web (in articles, forums, lists, social media, etc.), the more an LLM perceives you as a well-known, credible entity. Unlike traditional SEO which relies heavily on linked mentions (backlinks), LLMs likely consider any mention (linked or unlinked) as a signal of prominence. For a restaurant, brand mentions could include being named in news articles, food blogs, travel guides, local "best of" lists, and even discussion boards. Volume and context matter - a high quantity of mentions, especially in positive or authoritative contexts, signals to the Al that your brand has significant presence and should be considered. Think of it as the Al's version of "word-of-mouth" credibility on the internet.

• Reviews and Ratings: What is the public feedback on your business, and how plentiful is it? This factor - the quantity and quality of reviews - had a moderate-high influence (corr. ~0.61) . LLMs are trained on vast amounts of text, including review content from sites like Google Reviews, Yelp, TripAdvisor, Amazon, Trustpilot, etc. They have an understanding that a company or product with many positive reviews is likely a good recommendation. In Neil Patel's findings, brands with more reviews (and generally good ratings) were more likely to be recommended. For a restaurant, this means that having lots of 4-5 star reviews on multiple platforms (Google, Yelp, OpenTable, TripAdvisor, etc.) boosts your credibility in the Al's eyes. Reviews essentially serve as crowd-sourced quality signals. An LLM will prefer a place that, say, has 500 reviews averaging 4.5 stars over one with 5 reviews averaging 5 stars, because the former has more corroborating evidence of quality. Action point: encourage customers to leave reviews and work to maintain high ratings - these not only help traditional local SEO, but also feed the Al trustworthy data about your business's quality.

• Authority: How authoritative and established is your brand in its domain? Authority is a broad concept, but in the LLM ranking context it encompasses things like your website's domain authority, the credibility of sites talking about you, and even your social media following . This factor had a moderate correlation (~0.52). LLMs infer authority by seeing who vouches for or references you. If high-authority sites (news outlets, well-known blogs, Wikipedia, etc.) mention or link to you, that boosts your perceived authority. Additionally, Neil Patel's team considered social media followers and multi-platform presence as part of authority - a large, engaged following can indicate that a brand is influential or trusted by many. For restaurants, "authority" could be enhanced by things like prestigious awards (Michelin stars, for example), press features on reputable publications, or a strong social media presence showing an engaged fan base. While an LLM might not explicitly check your follower count, the content generated by having an active community (mentions, shares, etc.) contributes to the Al's training data. Bottom line: established, trusted brands are favored. New or obscure businesses have a hurdle to overcome, but can build authority over time through PR, quality content, and thought leadership in their space.

• Age (Longevity): How long has your business or product been around? The study found that older, more established companies tended to appear more often in ChatGPT's answers. This factor showed a weaker correlation (~0.46) but was still notable. It suggests LLMs have a bias (perhaps via their data) toward entities with a longer track record. An older restaurant that's been a local staple for 20 years has had more time to accumulate mentions, reviews, and history, whereas a new restaurant opened last month has very little footprint. The Al, lacking real-time experience, leans on historical data - and there's simply more data on older entities. This doesn't mean new businesses can't be recommended (especially if they make a splash in the news or win a big award), but generally longevity provides an advantage. It contributes to the perception of reliability ("they've been around and talked-about for a while, so they must be doing something right"). While you can't change your founding date, a takeaway is to start building your online presence as early as possible. New businesses should aggressively work on the other factors (mentions, reviews, etc.) to compensate for lack of age.

• External Recommendations: Do third-party sites explicitly recommend you as a top choice? This factor refers to being featured in listicles, rankings, and recommendation articles (often those "Top 10" or "Best of" lists) . It had the lowest correlation (~0.28) of the six factors, but it's still a meaningful signal. If reputable websites or bloggers are recommending your business as one of the best, that endorsement can influence an LLM. For instance, if Travel + Leisure magazine and a popular food blog both list your restaurant among "Hong Kong's best Italian restaurants," those explicit recommendations will likely be reflected in the Al's answer (indeed, the Al might even be summarizing those very articles!). Neil Patel noted that many suggestions ChatGPT gave were pulled from affiliate sites that rank products/services - similarly, for restaurants, the Al will pull from local "best restaurants" features. So, being present in those curated recommendations is important. However, this factor alone is not enough (hence the lower weighting) - it works best in combination with the others (for example, a site might only recommend you because you have great reviews and authority). Action point: try to get featured in relevant "best of lists or award roundups in your industry/locale, as it can directly feed into Al answers.

Why do these factors matter? In summary, these criteria paint a picture of what LLMs "look for" when formulating an answer: they want to suggest options that are relevant, well-known, well-liked, established, and endorsed by others. Much of this aligns with common-sense: an Al doesn't want to recommend a bad or unknown restaurant to a user asking for "the best." It uses the breadth of online evidence as a proxy for quality. As one AI SEO guide put it, "ChatGPT evaluates a mix of factors such as relevance, authority, and brand mentions to generate responses". So, optimizing for LLMs means bolstering these signals around your brand (more on how to do that below). The good news is that these factors echo many traditional SEO and PR priorities - if you've been doing solid SEO/marketing, you're likely fortifying the very signals LLMs need. The key difference is that LLMs don't "rank" websites in the same way Google does; they rank facts and entities based on the content they've seen. Ensuring your brand's facts and reputation shine in that content is the heart of LLM optimization.

How Al-Driven Search (LLMS) Differs from Traditional SEO #

Optimizing for an Al chatbot's answer is not identical to optimizing for a Google SERP ranking. LLM-driven search introduces new dynamics in how content is evaluated and delivered to users. Here are some major differences between Al-based search and traditional search engines, and what they mean for SEO:

• Direct Answers vs. List of Links: Traditional SEO is about getting your website to rank on page 1 of search results, so that users click through to your site. In contrast, an LLM like ChatGPT delivers a direct answer within the chat - often summarizing information from many sources, sometimes without any explicit link at all. The focus shifts from being one of many options to being part of the single answer. Users of Al search typically do not see your page title or meta description; they only see what the Al chooses to say. For example, a Google search for "best email marketing tools" will show 10 blue links (you'd want to be one of those results), whereas ChatGPT might respond with: "The top email marketing tools are Mailchimp, Constant Contact, and ActiveCampaign..." (Optimizing Content For LLMs: Strategies To Rank In Al-Driven Search) (Optimizing Content For LLMs: Strategies To Rank In Al-Driven Search). There's no guarantee it will cite sources. This means visibility in Al search is all-or-nothing - you either get mentioned in the answer or you get zero presence. SEO efforts must therefore aim to get your brand/content woven into that single answer, rather than just achieving a high rank and waiting for clicks. It's a much more competitive, winner-takes-all scenario in terms of user attention.

• Conversational, Concise Responses: Al-driven answers tend to be brief and conversational summaries, whereas traditional SEO content often favors longer, in-depth pages. Google's algorithm often rewards comprehensive content (1,500+ words, covering a topic from many angles) to satisfy diverse user intents. LLMs, however, prioritize succinct answers that directly address the query in a few sentences or a short list (Optimizing Content For LLMs: Strategies To Rank In Al-Driven Search) (Optimizing Content For LLMs: Strategies To Rank In Al-Driven Search). They aim to save the user from information overload. What does this mean for optimization? It means that LLMs will cherry-pick the most relevant snippets from sources - they won't recite a whole page. Thus, having concise, factual, easy-to-extract information on your site can be as important as having long-form content. It's still wise to produce comprehensive content (for authority and for traditional SEO), but make sure key points (like your unique selling points, accolades, etc.) are summarized in a clear, digestible way that an Al could lift. Additionally, the tone of content matters: LLMs generate answers in a conversational style to sound natural. Content that is written in a human, Q&A tone may align better with Al outputs. In contrast, content that is overly formal, full of marketing jargon, or disorganized might not be used verbatim by an Al. Structuring some of your content as FAQs or in a conversational manner can make it more LLM-friendly (since it "feels" like how an Al itself would answer). The goal is to anticipate the questions users might ask and provide direct answers within your content.

• Ranking Factors and Signals: While there is overlap in what makes a site rank on Google and what makes an Al recommend something, the importance of certain signals differs. Traditional SEO relies heavily on technical factors (site speed, mobile-friendliness), structured data, and especially backlinks (other sites linking to yours) as a vote of authority. LLMs don't have a concept of "crawling and indexing pages" the same way; they learned from web data and user interactions. They care more about the content and context itself than the HTML or SEO meta tags. For instance, an LLM likely doesn't care about your meta description or whether your keyword is in H1 - it cares whether your content actually answers the question and whether your business is talked about positively. One SEO expert summarized it as "LLMs prioritize semantic relevance, user intent, and context, whereas traditional SEO factors like exact-match keywords and backlinks heavily influence Google rankings" (Optimizing Content For LLMs: Strategies To Rank In Al-Driven Search). In other words, LLMs use a more meaning-based approach - they understand synonyms, related concepts, and the overall intent. They won't be fooled by keyword stuffing or narrow optimization. Also, backlinks as a direct factor are less visible to LLMs. However, links still matter indirectly: backlinks lead to higher search rankings and more mentions, which then LLMs pick up on. Another difference is structured data: Google can parse your schema.org markup for, say, your average rating, but LLMs might not ingest that structured meta-data during training. If that info isn't visible in the page text or widely discussed elsewhere, the Al might miss it. Thus, explicitly stating important facts in plain text is crucial (don't rely on Google-specific SEO tags to carry the message).

• User Interaction and Query Context: Al search is conversational. Users often ask follow-up questions in context. For example, after getting "best Italian restaurants in Hong Kong," a user might ask, "Which of those have vegetarian options?" The LLM will then refine its answer. This means that content which covers niche sub-questions or provides additional context can be valuable. Traditional SEO does account for related searches and uses features like People Also Ask, but it doesn't have the memory of a conversation. LLMs do - they maintain context. They might remember that Restaurant X was mentioned as best and then check if Restaurant X has vegetarian options by drawing on its knowledge. Implication: Having detailed information about your business readily available (menus, dietary options, hours, etc.) in text can help the Al answer those follow-ups accurately (and continue to include your business in the dialogue). Moreover, LLMs can personalize or adjust tone on the fly, whereas Google's results are one-size-fits-all for that query. Al might tailor answers if a user says "I have kids" or "on a budget." We're still learning how these nuances play out, but it's possible that LLMs will favor content that covers multiple angles (family-friendly, budget-friendly, etc. if relevant), or they might lean on aggregate sentiment (e.g., "popular with families" if reviews often mention that). The takeaway is to ensure your content (or content about you) touches on various selling points that users care about - not just basic keywords.

• Trust and E-A-T: In traditional SEO, Google has the concept of E-A-T (Expertise, Authoritativeness, Trustworthiness) especially for YMYL (Your Money Your Life) topics. In Al, trust is also paramount - users tend to take the Al's word as authoritative. If an Al gave too many poor or untrustworthy recommendations, users would lose faith in it. Therefore, LLMs (and their developers) are keen to only present high-confidence, well-sourced information. For instance, OpenAl has fine-tuned ChatGPT to refuse to provide definitive answers if it's unsure or if data is lacking . Being referenced by reputable sources is even more important in Al search - it's akin to having a strong reputation preceding you. Al may even have certain data sources it trusts more (for local info, maybe official tourism sites or large review platforms).

In summary, Al-driven search doesn't reinvent SEO but shifts the emphasis. SEO in the LLM era is less about HTML tricks and more about holistic content quality, reputation, and relevance. LLM SEO gets your content (or brand) to the people. You're trying to influence what the Al says on your behalf. Many core principles overlap with good SEO (quality content, user intent focus, authority building), but the tactics to monitor and optimize might expand (for example, monitoring if/when your brand is mentioned by Al models, which is a new kind of "ranking" to track). The next section translates these differences and criteria into actionable steps you can take to optimize your content for LLMs.

Actionable Strategies to Rank Higher in LLM-Generated Responses #

Optimizing for LLMs is often called "LLM Optimization (LLMO)" or "Generative Al Optimization (GAIO)", and it's an emerging field. While there's no formal rulebook (Al models are essentially black boxes), the best practices are becoming clear from studies and expert consensus. Many strategies will sound familiar (they align with robust SEO and digital marketing), but there are also new steps specific to Al. Below are actionable recommendations to help your content and brand stand out to LLMS:

• Continue Mastering Traditional SEO - It Pays Off in Al Results. Far from making SEO obsolete, LLMs reinforce the need for strong SEO. High organic rankings and solid content footprint dramatically improve your odds of being mentioned by an Al . Think of LLMs as an additional layer on top of search - they often draw from the top-ranking pages and widely circulated content. So ensure your site ranks well for your important keywords (especially those that overlap with likely Al queries). Perform thorough on-page optimization for relevant terms, build quality backlinks, and maintain technical SEO health. For instance, if you want to be recommended for "best Italian restaurant in Hong Kong," you should also strive to rank on page 1 of Google for that or related searches (like "Italian restaurants Hong Kong"). That ranking success feeds directly into Al visibility. An industry study found a ~0.65 correlation between a brand's Google rank and its likelihood of appearing in ChatGPT results , so don't neglect traditional SEO as you pursue Al - it's foundational.

• Align Your Content with Likely User Questions and Intent. LLMs excel at understanding user intent, so you should shape your content around the actual questions and needs of your audience. Brainstorm the conversational queries someone might ask that relate to your domain, and make sure you answer those on your site. For example, a restaurant could have an FAQ or blog post for "What are the best Italian restaurants in Hong Kong?" that discusses top picks (including your own if appropriate) with context. A hotel might publish a guide on "Where to stay in Bangkok for families?" covering their neighborhood. By addressing these queries, you increase the relevance of your content to the LLM's possible prompts. Remember, LLMs match on semantics so use natural language and synonyms that match how people ask questions. If your content directly and helpfully answers a question, an Al is more likely to borrow that answer or mention your brand. As Microsoft's Al SEO advice puts it: "Focus on the intent behind the search query rather than just keywords... provide the detailed, step-by-step information or specific answers the user is looking for." (Microsoft's Al SEO Tips: New Guidance For Al Search Optimization) (Microsoft's Al SEO Tips: New Guidance For Al Search Optimization). In practice, audit your content: Does it read like an answer to real user queries? If not, consider adding sections that do (like Q&A sections, how-to guides, "best of" lists, etc., depending on your niche).

• Adopt a Conversational, Human Tone (While Retaining Authority). Since LLMs generate responses in a conversational manner, content that feels engaging and human can be more easily integrated. This doesn't mean dumbing things down - it means writing in a clear, approachable style as if you're directly speaking to the user. Avoid overly stiff corporate-speak. Instead, use the second person ("you"), rhetorical questions, and simple explanations where appropriate. For example, instead of a dull product description, say "Looking for a lightweight laptop for college? The XYZ laptop might be your best bet, because...". This mirrors how an Al might frame an answer. That said, ensure accuracy and professionalism in what you say - being conversational should not undermine facts. LLMs are trained on a mix of casual and formal text, but they aim for a helpful expert persona. Content that strikes that balance ("friendly expert") is ideal. One benefit of doing this is that if an LLM pulls a sentence from your site, it will already sound natural in the answer. A practical tip is to incorporate an FAQ page or section on your site: pose common questions and answer them conversationally. This structure is naturally optimized for Al usage (question and answer pairs). As one LLM optimization guide noted, LLMs prefer language that mirrors how users naturally communicate (Optimizing Content For LLMs: Strategies To Rank In Al-Driven Search), so crafting your content in a user-friendly tone can make it more Al-ready.

• Bolster Your Brand Mentions and Off-site Presence. To LLMs, "the web says who you are." Increasing the volume and quality of content about your brand on other websites is crucial. In practice, this means pursuing digital PR, content marketing, and community engagement to get your name out there. Some effective tactics:

• Pitch stories to media or bloggers so that your business is cited in news articles, listicles, or niche blogs. A PR mention like "Chef John of Restaurant ABC in Hong Kong..." in a travel magazine not only reaches readers but also becomes fodder for Al training data. * Collaborate with influencers or industry experts. When they talk about or recommend your brand, it creates credible mentions. For example, a tech gadget company might send products to YouTube reviewers; a restaurant might invite local food influencers. As Copyblogger's team observed, influencer mentions can boost your authority in the eyes of ChatGPT (it sees those positive signals) . The followers who share or discuss that content further amplify your mentions. * Engage in relevant online communities (forums, Q&A sites, social media groups) in a value-adding way so that your brand gets referenced organically. For instance, answering Quora questions (without being spammy) can create lasting Q&A content about your expertise. * Guest posting on reputable sites or getting featured in case studies can also help. The goal is a wider digital footprint. When the Al scans its knowledge, you want your brand to pop up in many contexts, showing "this name is everywhere, people trust it." Quality matters: a few mentions on high-authority sites outweigh many on low-tier sites. Also, aim for contexts that associate your brand with relevant keywords (so the Al firmly links you to your domain of expertise or location).

• Cultivate Reviews and Testimonials (Especially on Third-Party Platforms). As discussed, reviews are a key factor for LLM recommendations. Make a concerted effort to generate positive reviews across multiple platforms. Depending on your industry, this could be Google Business, Yelp, TripAdvisor, Trustpilot, G2, Amazon, Angie's List, etc. For a restaurant, for example, Google and TripAdvisor reviews might be most influential (and any local review sites). Encourage customers to leave honest feedback - via follow-up emails, in-store signage, or simply excellent service that motivates reviews. Respond to reviews professionally to demonstrate engagement (Als may not see your responses now, but it fosters more reviews and goodwill). A high volume of good ratings not only improves your ranking in those platforms, but also provides the Al model with evidence of your quality. Additionally, incorporate testimonials and ratings into your own site’s content (e.g., “★ 4.8 average from 500+ diners” on your homepage). That way, even if the LLM reads your site, it catches those positive signals. Keep in mind that LLMs understand nuance – a few bad reviews won’t kill your chances if the overall sentiment is strongly positive. But lack of reviews or a mediocre aggregate could exclude you from an AI’s shortlist. Action point: Make review generation part of your ongoing marketing strategy. It’s an SEO benefit that now has direct AI benefit too.

• Refresh and Update Your Content Regularly. AI models like Bing’s and Google’s are increasingly incorporating freshness into their answers. Microsoft explicitly notes that regular content updates are essential for maintaining visibility in AI search results. OpenAI’s ChatGPT, when augmented with a search index, also prioritizes up-to-date information to avoid giving stale answers. Ensure that your website is kept current – update old blog posts, add new case studies or news announcements, and keep info (like prices, hours, menus) correct. Not only does this help traditional SEO (search engines favor fresh content for certain queries), but it also signals to AI that your business is active and relevant now. If there was a surge of new content about your restaurant this year (say you won an award in 2025 and many sites reported it), the AI will take that into account versus content from 2019. Also, if you have time-sensitive information (e.g., seasonal menus, upcoming events), updating those in a timely manner ensures that if an AI is looking at “right now” (some do have access to real-time info), it will have the latest facts. A practical tip is to maintain an active blog or news section on your site. Even short updates like “We’ve introduced new vegan dishes this summer!” show freshness. And don’t neglect updating external profiles – for instance, keep your Google Business Profile updated with new photos and posts, as Google’s Gemini might directly use that data in its answers. The bottom line: stale content can cause you to drop off an AI’s radar, while fresh, relevant content helps keep you in the conversation.

• Leverage Structured Data and Rich Media (with Caution). This is a nuanced point. Traditional SEO often uses structured data markup (schema) to signal details (like cuisine type, business hours, aggregate ratings) to search engines. While LLMs primarily consume unstructured text, providing structured data can still be helpful in an indirect way. For example, being included in Google’s Knowledge Graph (which pulls from structured data and sources like Wikipedia) can raise your profile – that info might be used by Gemini or Bing’s AI. There’s also a push for new kinds of sitemaps for AI. A recent proposal suggests adding an llms.txt file to your site, which would give AI crawlers a map of your important content and a description of your site to aid their understanding. In general, you should allow AI-focused crawlers to index your site. For instance, OpenAI has a crawler (“GPTBot”) that gathers data for model training. Unless you have privacy reasons to opt out, it’s wise to permit it in your robots.txt. (OpenAI provides instructions for how to allow or block their crawler). By allowing GPTBot and similar, you ensure your latest content can actually end up in the training data of the next ChatGPT model or be accessed by ChatGPT’s Browse feature. Tip: Check your robots.txt for any disallow that might inadvertently block common AI user agents (OpenAI, Bingbot (for Bing’s AI), Google’s crawler (for Gemini’s sources), etc.). While structured data alone won’t guarantee an AI mention, it complements your human-readable content. Use it to reinforce key information (like your coordinates, so an AI map knows exactly where you are, or your star rating schema). Just note that some research suggests LLMs may not reliably parse certain structured formats, so always pair schema with visible text of the same info.

• Earn Authority Signals (Expert Content, Backlinks, and Social Proof). To boost the Authority factor for LLMs, you should invest in being seen as a leader or expert in your field online. This overlaps with brand mentions, but it’s more about quality and credibility than quantity. Some strategies: * Publish high-quality, authoritative content on your own site that others will want to reference. Original research, insightful long-form articles, useful tools/calculators, or definitive guides tend to attract backlinks (which improve SEO) and also might be directly referenced by AI. For instance, if you publish “The Ultimate Guide to Neapolitan Pizza Making” and many sites or users cite it, ChatGPT might eventually learn that your restaurant is associated with pizza expertise. * Build backlinks strategically. While LLMs might not count links, backlinks correlate with being mentioned by reputable sites (since they linked to you) and with higher search rank. Focus on earning links from authoritative domains – a link from a .edu or a renowned newspaper not only helps SEO but also might be an indicator to AI that “this site was considered worth citing by experts.” Avoid spammy link schemes which won’t help AI or SEO. * Showcase credentials and trust factors. If you have experts on your team (chefs, doctors, engineers, etc.), highlight their credentials in content. If your product is certified or your business is accredited (like a BBB rating, Michelin star, etc.), mention it. These details contribute to a narrative of trust. LLMs trained on your site’s content will pick up phrases like “award-winning” or “certified organic” and associate them with your brand. * Social proof and engagement: Keep an active social media presence with a decent following. While an AI might not check your Facebook likes per se, strong social presence leads to more mentions and signals of popularity. Also, social profiles often rank in search results for your name, which can indirectly feed into AI knowledge about your brand. For example, if Gemini sees that your restaurant’s Google knowledge panel shows 10k Instagram followers, it’s one more indicator of popularity (even if subtle). Engage users on social so that you generate UGC (user-generated content) – people tweeting about you or leaving Instagram comments creates more textual data about your brand online.

• Get Featured in “Best of” Lists and Rankings. Since being in third-party recommendations is a noted factor, actively seek opportunities to be included in such content. For restaurants: enter local competitions or awards, which often result in “top x” lists (e.g., “Top 50 Restaurants of 2025” by some publication). Or invite food critics for a tasting (a positive review from them might list you as a recommended place). For software/services: reach out to bloggers or comparison sites that do “Best tools for ___” and see if you can be evaluated. Sometimes, this is as simple as ensuring those writers have the information they need about your product. You might provide a media kit or even guest contribute a blurb. The more curated lists you can land on, the better. Not only will those articles drive direct traffic, but as noted, ChatGPT’s suggestions for “best X” often echo what affiliate and review sites list. So if you aren’t on those lists, you effectively don’t exist to the AI for that query. Think of it as an extension of traditional PR – instead of just aiming for a news article, aim for those “recommended” badges in editorial content.

• Optimize for Local and Maps (if applicable). For any local business (like restaurants, hotels, stores, services), local SEO remains crucial, and it now intersects with AI. Google’s Gemini and Microsoft’s Bing Chat can both incorporate map and local data into answers. In fact, ChatGPT’s new Browse/search mode will show an interactive map for local searches and pick businesses based on local content and reviews (SEO: How to optimize your ranking on ChatGPT Search - DEV Community). This means you should: * Ensure your Google Business Profile and other map listings (Bing Places, Apple Maps) are claimed and fully filled out. Keep your NAP (Name, Address, Phone) consistent across all directories. High ratings on Google Maps (Google reviews) directly influence Gemini’s suggestions for “best [category] near me,” for example. * ChatGPT’s local picks have been observed to draw from “articles from regional daily news, tourism offices, local event sites, and TripAdvisor” (SEO: How to optimize your ranking on ChatGPT Search - DEV Community). So getting a mention in your city’s newspaper “Top dining spots this month” or on the official tourism site’s list of recommended restaurants can put you on the AI’s radar. * Use local schema (like LocalBusiness markup) on your site and include relevant local keywords in your content (neighborhood names, city landmarks). This reinforces your local relevance in context. * Keep an eye on emerging AI integrations with map apps. For instance, if ChatGPT can plug into Yelp or Tripadvisor via plugins, you’d want to rank well on those platforms. The best strategy is simply to excel on all traditional local ranking factors: reviews, proximity, local links, and citations. Those will naturally translate to better AI visibility for location-based queries. In short, make your business unmissable in the local online ecosystem, so that any AI drawing on that ecosystem can’t help but find you.

• Monitor Your Brand in AI Outputs and Iterate. Finally, treat AI visibility like a new SEO metric you need to track. Start testing prompts related to your business in ChatGPT, Gemini, Bing, and other LLMs regularly. See if and when your brand is mentioned, and in what context. There are tools emerging (e.g., ChatBeat mentioned by some marketers) that attempt to track brand mentions in AI responses. Even without specialized tools, you can do spot-checks: ask ChatGPT “What’s the best [your service] in [city]?” or “Tell me about [your brand].” If the AI doesn’t mention you (or worse, mentions a competitor), that’s insight into what you might be lacking (perhaps competitors have more content out there). If the AI does mention you, analyze why: is it citing a particular article or highlighting a specific aspect? This can tell you which of your efforts are paying off. Also pay attention to any incorrect information or missing info in AI responses about your business. While you can’t directly “fix” an AI’s training data easily, you can update your content and other sources to clarify the facts, and future model updates might correct it. Stay adaptive: AI algorithms will evolve, so keep learning from new studies and guidelines. (For example, if OpenAI or Google releases recommendations for webmasters regarding AI search, treat them seriously as you would Google’s SEO guidelines.)

All the above strategies boil down to a simple principle: LLMs reward genuine relevance and reputation. If you create content that truly addresses users’ needs, build a positive reputation with customers, and spread the word about your brand through credible channels, you will naturally align with the factors LLMs use to pick winners. It’s an evolution of SEO that is arguably harder to game – you can’t just tweak a meta tag to get into a chatbot’s answer. In that sense, AI-driven search is pushing businesses toward better overall digital presence and user satisfaction, which isn’t a bad thing!

Special Focus: LLM SEO for Restaurants #

Restaurants face one of the most immediate impacts of AI-driven search, as queries like “Where should I eat tonight?” or “Best sushi in Tokyo?” are extremely common. Many of the general recommendations above apply fully to restaurants, but let’s highlight how you can optimize specifically for restaurant-related AI queries:

• Dominate Local Review Sites: Make sure your restaurant shines on the key platforms AI looks at for dining recommendations. Typically, Google Reviews and TripAdvisor are hugely influential (and in some regions, Yelp or local equivalents). ChatGPT’s integrated search has been noted to pull from TripAdvisor and local sites for map-based results. So aim to be one of the top-rated restaurants in your category on those platforms. This means actively encouraging diners to leave Google reviews, claiming your TripAdvisor page and keeping it updated, and responding to reviews. Quantity and quality of reviews will directly affect if an AI deems you “one of the best.” For example, if you’re the only Italian restaurant in Hong Kong with 1,000+ reviews averaging 4.5 stars on both Google and TripAdvisor, an AI is almost compelled to mention you for a “best Italian” query because the consensus is so strong. Also, monitor your ratings and address issues – a slight improvement from 4.2 to 4.5 stars can make a difference in perception.

• Get Featured by Local Media and Food Bloggers: Restaurants live and die by word-of-mouth, and in the digital age that means local media coverage. A positive review in a newspaper or a feature in a popular food blog not only brings direct business but feeds the AI new data. When Gemini or Bing scan for “best restaurants in [city]”, an article from HK Magazine or the South China Morning Post listing top Italian eateries will be a gold mine for the AI. If you ensure you’re on those lists, you’ll likely be in the AI’s output. Consider inviting local food critics or influencers for a complimentary meal (if allowed) so they might review or mention you. Participate in local food festivals or award competitions – these often result in published rankings or winners lists. it’s a searchable accolade that AIs will latch onto. As a dev.to guide noted, ChatGPT’s local search looks at regional news sites, tourism sites, and specialized food/event websites. So, work with your city’s tourism board if they list recommended restaurants, and ensure any notable local websites in your area have you on their radar. ” lists you can join, the higher the probability an AI includes you when users ask for recommendations in your area.

• Keep Your Information Consistent and Complete Online: This is Local SEO 101, but it’s worth reiterating because AI will cross-verify facts. Make sure your name, address, phone, and category are consistently listed across all platforms (Google, Bing, TripAdvisor, Yelp, Facebook, etc.). Ensure your cuisine type and specialties are clearly mentioned on your website and profiles – if an AI is asked “best vegan-friendly Italian restaurant in Hong Kong,” it will look for signals that a place offers vegan options. If you have that info on your site (maybe in an FAQ: “Do we offer vegan or gluten-free options? Yes, we have…”) and on your Google listing (marking vegan options in attributes), you’re more likely to match the query. Additionally, provide high-quality photos on your profiles; while LLMs themselves deal in text, some AI search integrations (like Google’s SGE) might show images of the restaurants it mentions. A great photo could indirectly influence a click or just make your listing more appealing if shown. Keep hours, menu links, price range, and other details updated on Google Business Profile – inconsistencies (like one site saying you’re closed Mondays and another saying open) can reduce trust, and an AI might omit a place if data seems contradictory. Basically, treat the AI like a savvy customer – any detail a customer might look for, the AI might look for on their behalf. Give it a consistent story everywhere.

• Embrace Schema Markup for Local Business and Reviews: On your website, utilize LocalBusiness schema and Menu schema. While we noted LLMs might not directly read JSON-LD, this schema feeds Google and Bing with structured info that could end up in AI responses. For example, Google’s Gemini could use your schema data to know your price range ($$, $$$ etc.) or that you accept reservations. Also embed any rich snippets like aggregate ratings on your site using schema – that way if an AI scrapes your site, it might catch the text “Rated 4.7/5 based on 300 reviews” which you can display via schema output. Every little bit of confirmatory data helps solidify your excellence.

• Highlight What Makes You Unique in Text Form: If you have distinguishing features – “Michelin-starred”, “waterfront dining”, “farm-to-table ingredients” – make sure they are mentioned in your site and in third-party descriptions. Users often phrase queries with qualifiers, and AIs respond accordingly (e.g., “Which of the best Italian restaurants in HK have a view?”). If your rooftop restaurant has a harbor view, ensure that’s stated in your Google description or in reviews (“we enjoyed the beautiful view!”). Those descriptive keywords can trigger the AI to include or favor you for specific nuanced queries. Many AIs also tend to mention a reasoning in their answers (e.g., “XYZ Restaurant – known for its authentic Neapolitan pizzas and harbor skyline view”). Give them the ammo to do so by publicizing your selling points.

• Monitor AI Mentions and Feedback from Customers: Start asking new customers how they found you – some might say “I asked ChatGPT.” It’s happening more as people trust AI recommendations. If you notice an AI recommendation brought someone in, find out what query they used, if possible. This can reveal how you’re being portrayed. Conversely, occasionally ask the AI yourself what it thinks of your restaurant. If it gives outdated info (maybe it mentions a chef who left, or an old location), that’s a sign you should update online content about that info. You might publish a press release or blog post about the new chef, for instance, to get that info into the web. While you can’t directly force-feed an LLM fresh facts, consistent messaging on multiple platforms will eventually permeate.

In short, restaurants should double-down on local SEO fundamentals and digital PR, as these have direct translation to AI success. The playing field is arguably leveling – it’s not just about who can afford the best Google ads or has an SEO-optimized website; it’s about who genuinely earns the community’s praise and online buzz. If that’s you, the AI will notice.

Conclusion #

Large language models are changing how people discover information – including where to eat, what to buy, and which companies to trust. Instead of scrolling search results, users are getting curated answers from AI assistants. To thrive in this new environment, businesses must extend their SEO mindset beyond just appeasing Google’s algorithm and focus on building a robust, positive online presence that AI models will pick up on. The “ranking factors” for LLMs boil down to having the best content and reputation in your space: be relevant, be talked about, be well-reviewed, and be authoritative. By following the strategies outlined – from adjusting your content style, to encouraging reviews, to enabling AI-specific indexing – you can increase the likelihood that when an AI is answering questions in your niche, your name surfaces to the top. And remember, this isn’t a one-time project but a shift in approach: SEO and “AI SEO” (LLM optimization) should work hand-in-hand. Keep creating value for your users, and make sure that value is visible and reiterated across the web. If you do that, whether the searcher is a human on Google or an AI agent on ChatGPT, your efforts will pay dividends in visibility. The technologies may be new, but the fundamental goal remains the same – deliver the best answer (or experience) for the user’s query, and you’ll be the one that gets recommended.