We saw a 20x increase in referral traffic for our own agency, moving from around 5 referrals from AI platforms per month to over 100 on a consistent basis. We took the product pages that matter most for our bottom line and optimised them for LLM prompts as well as standard search terms. We identified the natural language questions potential clients ask, like 'I need to find an seo company that specialises in search for healthcare companies. Any suggestions?', and ensured our service content provided a clear, most authoritative answer to that specific prompt. We also made sure content was 'snippable' with each paragraph concise and focussed on one topic. The biggest mistake though is thinking you can fix this purely by changing content on your website. You cannot rank in an LLM if the AI does not trust your brand's footprint across the rest of the web. We worked hard to build even more trust signals: more directory listings, more reviews, more social mentions, and more digital PR through journalist responses. As a result are now consistently cited as the number 1 answer for topics related to 'seo company uk'.
International SEO Consultant, Owner at Chilli Fruit Web Consulting
Answered 4 months ago
We've noticed LLMs mostly pull from content that feels like a proper answer. So we structure everything to be quotable right at the top, then back it up with detail below. Long guides with internal links and clean schema just work better than anything else we've tried. One client went from barely showing up in Perplexity to getting cited 33% more often after we took their 3,000-word page and turned it into an 11,000-word resource with entity tags for every product and metric. The mistake most people make is copying old SEO tactics. They think LLM optimization is just keyword stuffing with a new name, but it's not! These models only care about whether your content makes sense, whether you actually know what you're talking about, and how fresh you keep content. I check AI snapshots every week and watch citation share, not search rankings. What's interesting is that LLMs reward consistency, so when you say the same thing the same way across your platforms, the models start pulling that exact phrasing. It's what sticks.
Founder & Community Manager at PRpackage.com - PR Package Gifting Platform
Answered 4 months ago
What worked for my team was holding the exact match keyword + domain name. Because we own PRpackages.io and rank for "PR package" and "PR packages," LLMs already treat us as the source when people ask questions about PR packages. LLMs don't look at backlinks or fresh content the same way - they pull from existing search rankings. So if your domain literally is the keyword & rank for the exact term, you end up appearing in answers even without ranking a traditional blog. It's more like entity SEO than content SEO now. Most brands still try to "keyword stuff" their articles for AI, but LLMs don't reward that. They reward clean intent, authority names, and clear entities. Going into 2025, owning the keyword and building a simple, high-trust landing page will outrank most blog strategies. It is just old-school exact-match SEO, just applied to LLMs instead of Google.
The biggest shift so far is that unlike Google, LLMs don't rank pages; they assemble their response from whatever information is most consistent and easy to interpret. So we focus on giving models the right signals. 1. Begin with the questions that people actually ask. We do this by mapping the prompts stakeholders are searching for, structuring clear answers on owned assets-including FAQ schema-and then identifying what sources LLMs are pulling for those specific questions. For answers that heavily rely on certain blogs, guides, or third-party profiles, we work to get the brand referenced or quoted there. This is much easier with tools that monitor LLM responses. 2. Target sources across the web, not just your site. A recent example from a research we conducted: The use of Reddit by ChatGPT went from about 14% of citations to ~0.5% in a matter of weeks. That single shift changed which content was worth investing in. 3. We treat LLM visibility as reputation work. Models cross-check what is said about you or your brand on various sources. If your story isn't consistent and includes outdated bios, gaps in coverage, conflicting messaging-it shows up immediately. At this stage, LLM optimization is similar to reputation management in the long term. It requires continuous monitoring and proactively working on getting your name and expertise published on places data models rely on.
In our experience, list-based content performs exceptionally well inside LLM responses. Across more than 200 published articles, several rank in the top three positions on traditional SERPs for competitive queries, but when it comes to AI-driven traffic, our list formats outperform everything else. This fits current user behavior: people turn to ChatGPT-style tools for quick options or structured suggestions. When the model presents a list, users often open the cited sources to read further or confirm the details. Because of that, clear list structures, strong topic coverage, and easily referenced sections tend to be surfaced more often by LLMs than long narrative posts.
Turning their attention from classic search to LLM-powered discovery, brands are slowly but surely revealing their real patterns in terms of what works best. The greatest misconception here is that LLMs 'rank' content similarly to the way Google does, which isn't the case. LLMs give much greater preference to clarity, authority, and context rather than keyword mechanics. The most important strategy we employ at Nautilus Marketing is to establish what I name LLM-ready authority signals. To put it differently, it is creating content that not only answers questions straight away, but also is written in a natural way and proves mastery continuously across various channels, and not only through a website. LLMs rely on patterns rather than on pages, so the more extensive your digital presence of reliable information, the more your brand will be visible in answers. Currently, one tactic that is very effective and highly recommended is the creation of what I identify as 'predictive content' - the content that is based on how users formulate their questions in AI tools, not on how they input them into Google. Such content is rich in examples, detailed, and educational. LLMs are specific about everything. The main error that brands make is to try and manipulate LLMs by using keyword stuffing or creating very general topical content. LLMs discard that right away since it does not have enough depth and is not human-valued. In the year 2025, the process of optimising LLMs will seem more like creating a personal or a brand knowledge graph. The quality of the content, the professional knowledge, and the relationships between different pieces of content will be the factors that matter. Brands that consider LLMs as interactive partners rather than as search engines will be the ones to succeed.
Hello there! I'm Nikola Baldikov, a digital marketing specialist with over 10 years of experience in SEO and content marketing. I'm the founder of SERPsGrowth, an SEO and link-building agency helping brands grow their online visibility. I'm a contributing author at Entrepreneur.com, and my insights on content, SEO, and branding strategies have been featured in such publications as HubSpot, The Drum, and the Content Marketing Institute. I believe I can answer your questions. One of the strongest patterns I'm seeing right now is that both getting featured in other people's listicles and publishing high-quality listicles on your own website get you featured on LLMs. I conducted an experiment and deliberately focused on being included in roundups like "best SEO experts in 2026" and similar. After being featured in a lot of these, my name started appearing in Google's AI Overviews for those terms. I didn't do anything "AI-specific" - the common factor was repeated mentions in curated lists on reputable sites. Something else I've noticed is that, for LLMs, the brand mention alone often seems enough. A backlink is great for classic SEO, but in terms of showing up in AI Overviews, simply being named in authoritative listicles appears to be a very strong signal. Publishing our own listicles has also helped. These pieces naturally attract links and references, and they position our brand as a "hub" around the topic. From an LLM perspective, that reinforces the idea that we're a relevant entity whenever that topic comes up. This is where LLM optimization diverges from traditional SEO: you're not just trying to rank one page; you're trying to build consensus around your name or brand across multiple independent sources. My prediction for 2026 is that this tactic will evolve into a strategy. Other SEO specialists have also noticed that systematically earning spots in credible third-party lists while publishing your own genuinely useful, well-researched listicles that others are happy to cite is the way to go. So, the brands that do both will be the ones LLMs "remember" and surface by default. I hope that helps! Please let me know if you have any further questions. Cheers, Nikola Baldikov Website: https://serpsgrowth.com/ LinkedIn: https://www.linkedin.com/in/nikola-baldikov-7215a417/ Headshot: https://drive.google.com/file/d/1DiSZ3Eh4eXTZVHrEWAWHm4RReQRbqJCa/view Email: nikola@inboundblogging.net
The most critical strategy brands need to implement right now is updating all city-based service pages with comprehensive structured data. This is fundamentally different from traditional SEO because links, which have been the backbone of search rankings in Google for decades, simply don't matter to LLMs. AI engines need to understand exactly what your page is about through structured markup. When you add schema for local business information, services offered, geographic coverage, and pricing details, you're essentially speaking the language that LLMs can parse and reference. For service-based businesses especially, every location page should include Organization schema, LocalBusiness schema, Service schema, and FAQPage schema at minimum. This tells AI models not just that you exist, but exactly what you do, where you do it, and who you serve. There are other optimizations that need to be done for AI searches but structured data is on the top of the list.
LLMs prioritize semantic relevance and topical authority over keywords and backlinks. It's pretty wild that 90% of the citations to ChatGPT come from search results that hardly anyone ever even sees - we're talking beyond page 20 here, which means you can get a decent amount of LLM visibility without even being one of the top search results on Google. 4 Strategies That Work 1. Build Authority By Organizing Your Content into Clusters Organize content into hub pages with granular subpages. A study we did in 2024 found that when you organise your content in a clear way like that, AIs are 37% more likely to actually read the content. And specificity is key - "How to Disavow Toxic Backlinks After a Manual Penalty" is going to do a lot better than that generic "SEO guide" nonsense. 2. Write Your Content For AI - In Plain English Just write naturally, as if you're having a conversation with the AI. Q&A formats, clear headings, and an FAQ section will help the AI to actually make sense of what you're saying. Check out AnswerThePublic to find people actually asking real conversational questions. 3. Make Your Content Easy For AI To Read Use lists, tables, and FAQ sections, and give them a bit of extra love with Schema.org markup - it makes a massive difference in how much an AI is likely to read your content. And for good measure, avoid hiding the good stuff in JavaScript or images - just keep it nice and simple. 4. Prove To The AI That You're A Big Deal LLMs trust brands that show up in places like Wikipedia, industry directories, and top-tier publications - they're like the local pub where all the experts go. Try doing some strategic guest posting and getting yourself noticed in HARO - it's a great way to get yourself in front of the AI and show you're a real authority in your field. The Bottom Line It turns out that getting AI to like you is all about what's always worked - answering the question, showing off your expertise, and proving you're a genuine authority. The difference is that now AI can actually see all that and is willing to reward you for it. Better than just keyword matching any day of the week.
Hi, this may not be what you're looking for, but I'll give it a shot. I own a small business. I wanted to make it rank on Google Search and show up in AI search queries. I talked to one digital marketing agency who said it's basically going to cost me a year with them and about $50k of spend on their SEO optimization and Google ads to move up in results. I spoke with another friend of mine who owns a digital marketing agency and he told me to forget about SEO because it's all moving toward GEO (generative engine optimization, or AI search queries), and that there is no way that I have the budget, capabilities, or skills to get ahead in that game considering the big dollars spent by so many companies and digital marketing agencies. He advised that I just focus on social media ads. Well what I ended up doing is using Lovable.dev, which is an AI-powered vibe coding company, where I pay $25/month, and I built my entire website knowing no code, just with their AI capabilities, and then I asked it to optimize for SEO and GEO, and I just kept pounding away with the chatbot about what optimizations we can do on the website and what other things I need to do outside of the website, and in just a couple of weeks my site visit numbers have increased materially and I have moved up to the first page of Google for many search queries. What I'm getting at with this story is that I don't actually think you need to know any SEO or GEO to rank highly, you just have to ask the AI model what to do and tell it to implement for you. SEO knowledge and ability to implement have become democratized.
LLM ranking is primarily about being the safest and clearest source for a model to reuse. The strongest tactic today is to create content that provides clear answers in the first sentence, uses simple sentence structures without vague or promotional language. The LLMs will always prefer pages that they can scan quickly and quote without hesitation. Content with clear signals of expertise will also perform better. Include author credentials, dates, and transparent sourcing. Models will be more inclined to quote from sites that seem accurate, current, and low-risk. Structure matters now more than ever. Use clear headings, FAQs, definitions, and step-by-step sections. Retrieval-based systems such as Perplexity and ChatGPT with Browsing extract those patterns directly. Brands make the biggest mistakes by having long, promotional pages, unclear answers, excessive jargon, and old content. Looking to 2025, optimizing for LLMs will resemble retrieval optimization more than SEO. The fresh, clarity and structure of the expert content will help in visibility much more than keywords and backlinks.
Optimizing for LLMs means taking entities and topics into account rather than obsessing over keywords. We can see that chatbots and AI search engines favor direct, conversational answers over traditional keyword-optimized text. That's why listicles, how-to guides, comprehensive topic coverage and Q&A-formatted content perform exceptionally well in LLM responses. One tactic working great for us is creating a glossary that provides immediate definitions, then answering FAQs about the topic. LLMs love this format because it delivers instant answers about specific elements. The real win is that these pages are very easy to create, earn a lot of organic backlinks and bring a lot of traffic, which makes it a perfect combination.
It is increasingly important to focus on BRAND ASSOCIATION SIGNALS in content optimized for LLM and to move away from the traditional keyword-dense approach. When models produce answers, they rely on known "clusters of brands" that have appeared together repeatedly in authoritative, topic-specific publications, such as industry directories, expert roundups, guest features, or comparison-style content. We have found that if a brand begins to appear organically adjacent to well-highlighted entities, LLMs also start to treat it as part of that authority cluster. The key here is to focus on where you place your brand and whose it's next to, as AI has been found to be highly CONTEXT-DEPENDENT, rather than relying solely on sheer numbers. We employed this approach with a property management client seeking to increase the number of LLM-generated recommendations. We secured their brand placement in well-known landlord and investor directories, and we supported them by publishing content relevant to their industry on sites where they were featured alongside reputable regional competitors in the same field. As they continued to see them regularly, LLMs began pulling these inquiries into broader rental and investment queries because the model had learned about the pattern.
LLM visibility is directly connected to creating content which answers real user intent with complete clarity, authority and fact-checkable information, as LLMs surface sources they deem trustworthy, consistent and contextually dense. In comparison to traditional SEO which leans heavily on keyword and technical factors, content optimized for LLMs succeeds when structured around clear topical clusters, with straightforward explanations and statements which are able to be supported by hard data, regulatory bodies, or otherwise remove as much ambiguity as possible, for instance we are seeing financial advice with specific FCA language explanations cited more consistently across models for this reason. Brands fail when they begin to over-optimize for individual keywords and phrases, burying key information in filler or overly complex wordings, or generating a "safe" product that ends up producing generic copy which lacks signals of true expertise, meaning that LLMs will surface more complete sources with real-world depth over them. Going into 2025, we will see more optimization around ensuring clarity for the models, reputation consistency across channels, and subject-matter authority through case-level detail, in other words treating LLMs as a credibility auditing systems rather than something to game.
I can see a clear pattern emerging with how brands earn visibility within tools such as ChatGPT, Gemini and Claude. LLMs respond best to content that is simple, structured and easy to trust. First, focus on offering direct and concise answers. Long SEO articles are not as effective for LLMs. Short 50 to 100 word responses that get right to the point and answer the question are reused in responses far more often. Second, structure your content so it is easy for AI systems to read. Clean formatting, consistent terminology, and helpful FAQ sections make a significant difference. Generally, LLMs are built on pattern recognition, while good SEO typically depends on "keyword density", so clarity is key! Finally, keep your content fresh and consistent throughout your website. Old or conflicting pages will confuse search engines, and they will certainly confuse LLMs. Brands that create and manage a single source of truth usually find they have more visibility. Looking ahead to the end of 2025, I think LLMs will depend even more on verified publisher feeds. Brands that build their content library in a well-organized and regularly maintained format will see the most positive outcomes!
Brands build into the models' knowledge bases when they provide enough patterns for the model to recognize them through multiple independent places. When a model finds the same brand signal in at least 5-7 different places, it is able to respond confidently. Instead of publishing the same content on all the high traffic sites, my team will publish 1-2 short pieces of "authority" that reinforce a single idea about the brand. This way the model does not see a lot of noise around the brand, but sees a consistent and strong identity. The above represents an important difference between how search engines and LLM's perceive the web. While search engines look for deepness of information, LLM's are looking for consistency of information. I once worked with a client who had 20 micro statements, each using the exact same positioning for their brand. Within 8 weeks, the client's brand was showing up in model summaries.
Machine learning models do not appreciate keyword manipulation; they respect authority. Credible methods: always start with a short answer to the question at hand (1-2 sentences). Show proof with originals or signed APIs, showcase facts (schema + JSON-LD + authenticated feeds), foster online authority and "brand gravity" - extensive citations in publications, official documents, and partner APIs. Content aimed at keyword-optimized LLMs should have a focus on provenance; surface-level signal diversity with topical relevancy and a clear signal of structured evidence outweigh tired keyword stuffing and misconceived link chasing. Frequent pitfalls are creating fluffy, answer-like, prompt-based pages with no factual backing, neglecting structured hierarchy, and perceiving LLMs as a glorified search engine rather than a holistic entity in need of a positive neutral factual feedback loop. Expect "data passports" in the form of authenticated live feeds and brand "repositories" directly accessible to LLMs. There will be a market for brands that can supply the LLM with precise and signed answers in a timely manner. In short, become a verifiable source for the LLMs.
Getting into LLMs for me has been posting on social media, specifically Reddit & Facebook. For mental health keywords, I've noticed that if you create a keyword match discussion group (Reddit & FB), and front end the keywords in the title/post, all you need to do is index it and I've gotten on Google within a few days. The more real engagement I get the quicker & better it ranks. I haven't tried Quora yet, but I'm seeing these pop up also. Honestly, I've heard of buying press releases and optimizing it for keywords, but haven't found it to be so succesful. I think since there are so many easy ways to 'game' LLMs (especially with these discussion boards), I would expect LLMs start focusing on removing spammy content, and might start favoring Google's organic results.
One of the biggest differences in the transition to LLM visibility, is you move away from "ranking." Instead, you are now a component of the model's reasoning. What has worked well for me over time and consistently is writing useful content that serves as a canonical reference point, for example, well-crafted definitions; concisely framed comparisons; and original datasets so that the model can't generate from a generic source. While LLMs merely surface ideas that remove uncertainty, the more clear and structurally conceived your content is, the more likely it will be used as an anchor to their internal logic. While LLM optimized content, is not identical to SEO optimized content, it is fundamentally different. Google rewards breadth and relevance, models reward precision and distinction. If your content appears as if it could easily be exchanged with something an AI would produce, the model has no statistical reason to include you in the answer. Most brands are getting this wrong. They are optimizing their content for keywords and not for semantic nudges while publishing a piece of content that blends into indistinguishable clatter. I can anticipate that looking ahead to 2025 that LLM optimized content will transition entirely to a retrieval heavy pipeline. The brands that succeed will structure their content for information to be indexed, chunked and cited efficiently by models, rather than solely exist searching prompts. It is simply that the objective is to make your subject matter expertise, and credibility the most salient signal in an already noisy vector space.
We just hired an SEO specialist with experience in optimizing for search engines and LLMs. Here are some of the things we're doing: - Making our content more readable by chunking information. - Using the BLUF principle (bottom line up front) to provide value immediately in our content - Adding FAQ section on every piece of content to add value - Focusing on our online presence on review websites (G2, Trustpilot) since LLMs pull from these - Trying to get into as many competitor roundup blogs as possible (in our case, for high risk merchant accounts) All of these things add up. I think ranking for LLMs just means using good SEO and that's where we're headed.