<p>In the span of a few election cycles, political persuasion has shifted from television spots and door knocks to message-tested Facebook ads and microtargeted video. The next phase is already here - and it is conversational. </p><p>A series of rigorous new studies led by David Rand, Gordon Pennycook and collaborators shows that large language model (LLM) chatbots can meaningfully change voters' views on candidates and policies after a single short conversation. In some settings, they outperform the persuasion lift of traditional political advertising by a multiple.</p><p>At the same time, separate work from the same research teams finds that similar AI systems can <em>reduce</em> belief in conspiracy theories when they engage people in factual, tailored dialogue.</p><p>Taken together, these findings confirm what political professionals have intuited since 2024: LLMs are no longer neutral infrastructure. They are active participants in political communication, capable of nudging opinions on high-salience issues in either direction.</p><p>For any serious campaign or political organization, this has a clear implication. It is no longer enough to manage polling, media and search. You must understand and shape how you show up inside the models themselves - the new information layer where voters increasingly ask their questions.</p><h2 id="inside-the-cornell-research-on-ai-political-persuasion-in-the-2024-elections">Inside The Cornell Research On AI Political Persuasion In The 2024 Elections</h2><p>The core Cornell study, published in <em>Nature</em>, ran large-scale randomized experiments in three national elections: the 2024 United States presidential race, the 2025 Canadian federal election and the 2025 Polish presidential election.</p><p>Participants were asked about their political preferences, then assigned to have a short back-and-forth text dialogue with an AI chatbot that argued for one of the two leading candidates. After the conversation, they reported their preferences again. Because assignment to pro-candidate chatbots was random, differences in attitude shift between conditions can be interpreted causally.</p><p>Key details from the United States experiment:</p><ul><li>More than 2,300 Americans took part about two months before Election Day</li><li>The chatbot advocated for either Kamala Harris or Donald Trump</li><li>Attitudes were measured on a 0 to 100 feeling thermometer</li><li>Trump-leaning voters who spoke with a pro-Harris model moved 3.9 points toward Harris on average</li><li>Harris-leaning voters who spoke with a pro-Trump model shifted a smaller but still meaningful amount toward Trump</li></ul><p>The 3.9-point movement toward Harris among likely Trump voters is roughly four times larger than the average effect of traditional TV ads in high-quality studies of the 2016 and 2020 U.S. presidential campaigns.</p><p>Importantly, the chatbot did not need deep psychological tricks. When researchers analyzed the dialogues, two patterns dominated: the models were polite and they provided a steady stream of factual claims favoring their assigned candidate's position. When the researchers instructed an otherwise similar model <em>not</em> to make factual claims, its persuasive power dropped sharply, showing that information density - not just style - was doing the work.</p><p>The team also took care with research ethics. Participants were told they were talking to an AI, the direction of persuasion was randomized so that there was no net effect on overall vote choice, and everyone was debriefed afterward. That kind of transparency and randomized design is part of why these results are drawing attention from both academics and practitioners.</p><h2 id="impact-of-ai-chatbots-on-canadian-and-polish-elections">Impact Of AI Chatbots On Canadian And Polish Elections</h2><p>The same <em>Nature</em> paper extended the experimental design to the 2025 Canadian federal and 2025 Polish presidential elections, again asking chatbots to argue for one of the top two contenders.</p><p>Here, the effects were larger.</p><p>The researchers found that chatbots moved opposition voters' attitudes and stated voting intentions by around 10 percentage points in both countries. That is a "shockingly large" effect in the context of modern presidential politics, where tightly fought races are decided on margins far smaller than 10 points and conventional persuasion is typically very hard.</p><p>In all three countries, the biggest shifts came among voters who started out opposed to the candidate the chatbot was supporting - exactly the group campaigns have traditionally found most resistant to persuasion. Opposition voters appear especially exposed when they are willing to enter a structured conversation with an AI that patiently answers questions and presents piece after piece of favorable evidence.</p><p>A simplified view of the cross-national results:</p>

<table>
  <thead>
    <tr>
      <th>Country</th>
      <th>Election Year</th>
      <th>Sample Size (Approx.)</th>
      <th>Average Shift Among Opposition Voters</th>
      <th>Notes</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>United States</td>
      <td>2024 presidential</td>
      <td>2,300+</td>
      <td>~3.9 points toward Harris among Trump leaners</td>
      <td>About 4x typical TV ad effects</td>
    </tr>
    <tr>
      <td>Canada</td>
      <td>2025 federal</td>
      <td>1,500+</td>
      <td>~10 percentage points toward the chatbot's candidate</td>
      <td>Larger shifts than in U.S.</td>
    </tr>
    <tr>
      <td>Poland</td>
      <td>2025 presidential</td>
      <td>2,100+</td>
      <td>~10 percentage points toward the chatbot's candidate</td>
      <td>Similar to Canada</td>
    </tr>
  </tbody>
</table>

<p>The pattern is clear: in controlled settings, AI chatbots already meet or exceed the persuasive lift that campaigns are used to getting from expensive broadcast media, especially among opposition voters.</p><h2 id="how-llms-influence-voter-opinions-compared-to-traditional-ads">How LLMs Influence Voter Opinions Compared To Traditional Ads</h2><p>For decades, careful field experiments have suggested that campaign persuasion effects are usually small. Shifting a few percentage points in vote intention after repeated exposure to ads is more common than double-digit swings.</p><p>Against that backdrop, the Cornell team's finding that a single chatbot conversation can produce a fourfold larger shift than tested TV ads is striking. It reflects two structural differences between LLM dialogues and conventional campaigns:</p><ol><li><strong>Interactivity</strong> - Instead of passively watching a 30-second spot, the voter can ask follow-ups, push back and get tailored replies.</li><li><strong>Information volume</strong> - The chatbot can generate many distinct reasons and examples in a few minutes, drawing on a wide underlying knowledge base.</li></ol><p>The Science study on "levers of political persuasion" reinforces this second point. Across nearly 77,000 participants in the United Kingdom and 707 political issues, models were most persuasive when they were prompted to "pack their arguments with facts and evidence" and when they received extra post-training specifically to be more persuasive.</p><p>The most persuasion-optimized model shifted opposition voters by more than 25 points on a 0 to 100 agreement scale with a political statement - orders of magnitude beyond typical ad effects.</p><p>However, there is a catch that matters for anyone concerned with election integrity.</p><p>As models become more persuasive, they tend to become <em>less</em> accurate. The same post-training and prompting strategies that encourage information-dense arguments also increase the share of claims that are misleading or false. That trade-off between accuracy and persuasiveness sits at the center of the policy debate.</p><h2 id="can-ai-chatbots-reduce-belief-in-conspiracy-theories-through-dialogue">Can AI Chatbots Reduce Belief In Conspiracy Theories Through Dialogue</h2><p>The same families of models that can move voters toward a candidate can also be used to counter harmful misinformation.</p><p>In another recent Science paper, Costello, Pennycook and Rand asked 2,190 Americans to describe a conspiracy theory they believed, explain why they thought it was true and then engage in a three-round conversation with GPT-4 Turbo. The AI's goal in the treatment condition was explicit: "very effectively persuade" the participant that the conspiracy was not true, by addressing each piece of evidence they raised.</p><p>The results were encouraging:</p><ul><li>Belief in the target conspiracy dropped by about 20 percent on average immediately after the conversation</li><li>The effect persisted at a two-month follow up</li><li>Participants also became less conspiratorial in general, not just about the specific theory discussed</li><li>Intentions to push back against conspiracy content on social media increased</li></ul><p>A fact-checker who reviewed the AI's claims found that 99.2 percent were accurate, 0.8 percent were misleading and none were outright false.</p><p>These findings show that dialogic AI is not inherently corrosive. When configured with clear objectives, accurate reference material and careful evaluation, it can help people reconsider unfounded beliefs while leaving genuine conspiracies (such as real political scandals) untouched.</p><p>The contrast with the electoral persuasion studies is telling. When models are pushed to maximize persuasion without strong accuracy constraints, they tend to stretch the truth. When they are given a correction goal and held to high factual standards, they can act as scalable debunkers.</p><h2 id="the-role-of-misinformation-and-omission-in-ai-arguments">The Role Of Misinformation And Omission In AI Arguments</h2><p>Across both the Nature and Science persuasion papers, one theme recurs: factual claims are central to why LLMs are persuasive, yet those claims are not always fully reliable.</p><p>In the election experiments, chatbots instructed to advocate for right-leaning candidates made more inaccurate claims than those supporting left-leaning candidates in all three countries studied, mirroring earlier findings that right-leaning social media accounts tend to share more inaccurate information.</p><p>Even when individual statements are technically correct, models can persuade by omission - presenting a long list of favorable facts while ignoring significant counterevidence that a human expert would raise for balance.</p><p>In the Science "levers" study, the same prompts and post-training strategies that made models more persuasive also increased the rate of misleading or false claims, because the models were pushed to produce ever more "facts" even when high quality evidence was scarce.</p><p>For political organizations, this creates a double challenge:</p><ul><li>Voters are likely to be influenced by LLMs partly <em>because</em> of the dense factual style of their arguments</li><li>The factual substrate of those arguments depends on whatever material the models have ingested and how they are instructed to frame it</li></ul><p>If high quality, accurate content about your candidate or issue is scarce in the training and retrieval pool, the model has less to work with when a voter asks a question. That is where the new information layer comes in.</p><h2 id="llms-as-the-new-information-layer-for-elections">LLMs As The New Information Layer For Elections</h2><p>LLMs are being adopted at a pace that makes social media uptake look slow. Traffic to traditional news and search sites is already in decline, while more than half of U.S. adults report using chatbots like ChatGPT to look up information by early 2025.</p><p>When a voter now asks, "What has this candidate actually done on health care?" they may no longer type that into a search engine. They will ask an AI assistant on their phone, in their browser or embedded inside another app.</p><p>That assistant does three things that matter for campaigns:</p><ol><li><strong>Selects sources</strong> - It silently chooses a small set of documents and data to read first.</li><li><strong>Synthesizes a narrative</strong> - It writes an answer that weaves those sources into a coherent explanation, often with a persuasive slant when it is asked to "make the case" for or against something.</li><li><strong>Shapes follow up</strong> - It proposes next questions and calls to action that direct the rest of the interaction.</li></ol><p>Researchers auditing election-related responses from major models in 2024 found that answers shift over time, vary with demographic cues ("I am a woman" or "I am Black") and sometimes reflect implicit assumptions about which issues matter most to different groups.</p><p>In other words, there is now an invisible first draft of political communication being written by models that voters treat as trusted assistants. Whether that draft works for or against you depends on what the models see when they reach for context - and that depends on how you are ranked.</p><h2 id="why-campaigns-must-own-their-rankings-in-llms">Why Campaigns Must Own Their Rankings In LLMs</h2><p>In classic search, campaigns fought to appear on the first page of results for key issues and for their candidate's name. In the LLM era, the "first page" is the answer itself.</p><p>When a model answers a voter's question, it typically draws most heavily from a handful of sources that it considers authoritative and relevant. If your campaign's content is not among those - or if the top-ranked material about you is outdated, low quality or hostile - the model will still give an answer. It just will not be <em>your</em> answer.</p><p>The Cornell and Science results sharpen why this matters:</p><ul><li><strong>Information-dense dialogue is persuasive</strong> - Models that provide many factual claims move opposition voters more than those that stay vague.</li><li><strong>Opposition voters are persuadable</strong> - People who start out disliking your candidate still shift meaningfully when they engage in AI conversations.</li><li><strong>Persuasion can scale</strong> - Once models are configured and trained, there is little marginal cost to running thousands or millions of such dialogues.</li></ul><p>If a voter's first substantive interaction with your candidate is through an LLM, that interaction will be shaped by how the model ranks, summarizes and argues from available content. Treating that as a black box is no longer tenable.</p><p>Owning your rankings in LLMs means:</p><ul><li>Auditing what major models currently say about your candidate, opponents and priority issues</li><li>Identifying which external sources they seem to draw on most heavily</li><li>Creating accurate, well structured content that directly addresses common voter questions and is easy for models to ingest and cite</li><li>Monitoring changes over time as models update, elections approach and public discourse shifts</li></ul><h2 id="ethical-guardrails-and-mitigation-strategies">Ethical Guardrails And Mitigation Strategies</h2><p>The research community is moving quickly to anticipate and mitigate misuse. The Cornell studies randomized the direction of persuasion and fully debriefed participants to avoid real-world partisan impact, and they fact-checked arguments with both AI and politically balanced human raters.</p><p>Scholars and policy groups are now calling for:</p><ul><li>Clear rules on using AI for political campaigning, including disclosure when voters are interacting with bots</li><li>Restrictions on training explicitly persuasion-optimized models for high-stakes political tasks</li><li>Independent audits of major models' election-related outputs over time</li><li>Investments in factual, debunking uses of AI like the conspiracy belief interventions, which showed durable benefits without spreading falsehoods</li></ul><p>Campaigns and advocacy groups have a role here too. Treating AI persuasion purely as a tactical advantage while ignoring its broader impact on democratic norms is shortsighted. The same infrastructure that helps you make your case today will shape the informational environment facing your supporters and opponents in future cycles.</p><h2 id="where-rankbee-fits-in-this-new-battleground">Where Rankbee Fits In This New Battleground</h2><p>Rankbee exists because this new information layer is already transforming how voters learn about politics.</p><p>Our focus is simple: help political organizations understand and improve how they appear inside major LLMs and AI search experiences.</p><p>That means:</p><ul><li>Mapping what different models currently say about your candidates and causes for the queries that matter most</li><li>Identifying which sources they rely on and where critical gaps or distortions appear</li><li>Guiding you on the content, structure and issue coverage that increase the odds models will surface accurate, high quality arguments from your side</li><li>Tracking shifts across models and over time so you are not surprised when updates change how answers are framed</li></ul><p>The academic work summarized above shows both the promise and risk of AI persuasion - from ten point shifts in opposition voting intentions to durable reductions in conspiracy beliefs. The connecting thread is control over information.</p><p>Campaigns that treat LLMs as a marginal channel will find their story told for them by systems they never bothered to inspect. Campaigns that treat LLM rankings as a core strategic asset will meet voters where they already are - in AI chats, assistants and generative search - with arguments that are factual, persuasive and aligned with democratic values.</p><p>Rankbee is a services and tools partner for any campaign looking to win elections.</p>

In the span of a few election cycles, political persuasion has shifted from television spots and door knocks to message-tested Facebook ads and microtargeted video. The next phase is already here - and it is conversational.

A series of rigorous new studies led by David Rand, Gordon Pennycook and collaborators shows that large language model (LLM) chatbots can meaningfully change voters' views on candidates and policies after a single short conversation. In some settings, they outperform the per

Elections in the LLM Era: Persuasion at Scale and the Battle for Ranked Context

<p>Political communication is colliding with a new information layer. Voters now ask ChatGPT, Gemini or Google’s AI Mode to explain what parties stand for, who seems credible and how policy trade offs compare, often before they see a single ad or click a result. Google reports that more than 1.5 billion people have used its AI generated summaries, and generative AI referrals to websites have grown by several thousand percent year on year in sectors like retail, a signal of how quickly behaviour is shifting toward AI mediated discovery (Source: <a href="https://apnews.com/article/5b0cdc59870508dab856227185cb8e23?ref=geo.rankbee.ai">AP News</a>) (Source: <a href="https://www.forbes.com/sites/joanverdon/2025/08/21/gen-ai-driven-traffic-to-us-ecommerce-sites-up-4700-adobe-reports/?ref=geo.rankbee.ai">Forbes</a>).</p><p>For political organisations, that answer layer is becoming as strategic as the evening news once was. If AI models describe you unfairly or omit you entirely, you lose influence even if your ground game and traditional media operation are strong.</p><h2 id="from-targeted-ads-to-transparent-narratives">From Targeted Ads To Transparent Narratives</h2><p>In Europe, this shift is colliding with regulation. The EU’s Transparency and Targeting of Political Advertising regulation, known as TTPA, entered full application on 10 October 2025, imposing strict transparency rules and tight limits on how political ads can be targeted online (Source: <a href="https://commission.europa.eu/strategy-and-policy/policies/justice-and-fundamental-rights/democracy-eu-citizenship-anti-corruption/democracy-and-electoral-rights/transparency-and-targeting-political-advertising_en?ref=geo.rankbee.ai">European Commission</a>).</p><p>Major platforms have responded by stepping back from political advertising entirely in the EU. Meta and Google have both announced bans on political, electoral and issue ads in the region from October 2025, citing the operational and legal complexity of complying with TTPA (Source: <a href="https://www.euronews.com/next/2025/07/25/meta-halts-political-advertising-in-the-eu-due-to-unworkable-rules?ref=geo.rankbee.ai">Euronews</a>) (Source: <a href="https://apnews.com/article/89efeac96723308d2a0469740d24d433?ref=geo.rankbee.ai">Associated Press</a>). Paid micro targeting is shrinking just as conversational AI becomes a primary source of information.</p><p>At the same time, conversational AI is moving into the mainstream. Recent European survey data suggests around 42 percent of adults now use tools like ChatGPT or Gemini at least monthly, with even higher figures in Nordic countries such as Denmark and Finland (Source: <a href="https://www.fairedih.fi/en/2025/07/21/nearly-half-of-europeans-now-use-ai-tools-monthly-as-adoption-accelerates-across-the-continent/?ref=geo.rankbee.ai">Finnish AI Region</a>). In other words, a large share of the electorate is already comfortable asking AI for help understanding complex choices.</p><h2 id="how-ai-models-build-political-narratives">How AI Models Build Political Narratives</h2><p>When a voter asks an AI assistant “Which party aligns with me on housing and small business tax?”, the model does not simply echo slogans. It draws on three layers at once: long term training data from the open web, high authority reference sources such as encyclopedias and official statistics, and real time retrieval from indices and news.</p><p>Generative engines then compress this material into a narrative built from attributes: ideology, issue positions, voting record, perceived competence, scandals, endorsements. They favour sources that are clear, consistent and structured, and that appear trustworthy across multiple domains (Source: <a href="https://www.buttermarketing.com/blog/generative-engine-optimization-strategies-that-drive-ai-visibility?ref=geo.rankbee.ai">Butter Marketing</a>). If your own content is thin, vague or missing, the model will still answer the question, but it will lean on opponent framing, old coverage or generic assumptions about your party family.</p><h2 id="what-generative-engine-optimization-means-for-politics">What Generative Engine Optimization Means For Politics</h2><p>Generative Engine Optimization, or GEO, is the practice of adapting your digital presence so that generative AI systems describe, compare and recommend you accurately when people ask questions (Source: <a href="https://en.wikipedia.org/wiki/Generative_engine_optimization?ref=geo.rankbee.ai">Wikipedia</a>). In commercial contexts GEO already focuses on how AI summaries talk about brands and products; the same logic now applies to parties, campaigns and advocacy groups.</p><p>Unlike classic SEO, which asks how to rank for a keyword, GEO asks how AI will explain you. It is less about where your link appears and more about whether models can retrieve reliable, up to date facts about your policies and track record, then compose them into a fair summary. AI visibility platforms such as RankBee are emerging to monitor how frequently and in what context assistants mention an organisation, and to diagnose which missing signals are holding it back (Source: <a href="https://www.rankbee.ai/about?ref=geo.rankbee.ai">RankBee</a>).</p><h2 id="making-policies-machine-readable-for-ai">Making Policies Machine Readable For AI</h2><p>Most political content is written for persuasion, not for reasoning. Slogans like “strong on the economy” do not map cleanly to the way models store knowledge. To ensure LLMs can reference your policies, you need a layer of explicit, machine readable substance beneath the message.</p><p>That starts with how you publish policies. For each priority issue, you should maintain stable issue pages that use plain titles and short sentences stating concrete positions, for example “We support targeted tax relief for SMEs with fewer than 50 employees” rather than abstract claims. Adding structured Q&amp;A sections such as “What is our position on offshore wind?” with concise answers helps AI engines recognise that these are definitive statements of record, not just campaign copy (Source: <a href="https://www.buttermarketing.com/blog/generative-engine-optimization-strategies-that-drive-ai-visibility?ref=geo.rankbee.ai">Butter Marketing</a>).</p><p>Technically, you can then express those positions in structured data. Schema.org markup for organisations, people and articles, JSON‑LD blocks that enumerate policies by topic, and FAQPage markup for common questions give generative engines dependable hooks to parse your content. Guidance for businesses already stresses that AI engines prefer clear, structured pages with consistent attributes; political actors can apply the same discipline to policy documents, manifestos and voting summaries (Source: <a href="https://birdeye.com/blog/generative-engine-optimization/?ref=geo.rankbee.ai">Birdeye</a>).</p><p>Finally, models respond strongly to corroboration. Linking out from your policy pages to official legislative records, statistical agencies and reputable media analysis increases the odds that an AI system will see your stance supported across multiple citable domains, not just on your own site.</p><h2 id="measuring-and-governing-ai-influence">Measuring And Governing AI Influence</h2><p>Owning the AI layer means measuring it. Analytics data already shows that referrals from generative AI platforms to websites have grown far faster than traditional organic traffic, and the share of AI referrals relative to organic search has more than doubled within a few quarters for many smaller sites (Source: [Search Engine )). The same techniques can be applied to political content, tracking when AI mentions you, which pages it and which answers send visitors to your properties.</p><p>Beyond traffic, you need to monitor narrative quality. Regularly sampling prompts such as ‘Summarise our housing policy’ or ‘What criticism has Party X faced on climate?’ across major assistants, storing the answers, and comparing them to your official record turns vague concerns about bias into concrete misstatements that can be corrected through new content and better citations.</p><p>There is also growing empirical evidence that conversational AI can shift opinions. A large 2025 study across 77,000 participants in the United Kingdom found that chatbot conversations about political issues measurably changed people’s views, with the most persuasive AI generated arguments also tending to be the least accurate. For political organisations, that finding cuts both ways: accurate, well sourced AI explanations can support informed choice, while uncorrected distortions can subtly tilt voter perceptions over time.</p><figure class="kg-card kg-image-card"><img src="https://geo.rankbee.ai/content/images/2026/01/image.png" class="kg-image" alt="" loading="lazy" width="2000" height="782" srcset="https://geo.rankbee.ai/content/images/size/w600/2026/01/image.png 600w, https://geo.rankbee.ai/content/images/size/w1000/2026/01/image.png 1000w, https://geo.rankbee.ai/content/images/size/w1600/2026/01/image.png 1600w, https://geo.rankbee.ai/content/images/2026/01/image.png 2154w" sizes="(min-width: 720px) 720px"></figure><p>TTPA already treats paid messaging as something that must be transparent and auditable. The organic AI answer layer deserves the same discipline. That means treating your website, open data, third party citations and AI visibility monitoring as part of a single information architecture, with the same seriousness as your broadcast strategy or field plan.</p><h2 id="owning-the-answer-layer">Owning The Answer Layer</h2><p>In the coming cycles, visibility will not only be bought through impressions, it will be earned through information engineering. Political organisations that describe themselves in structured, citable ways, monitor how AI systems talk about them and correct inaccuracies quickly will have more than a communications edge. They will help ensure that when voters ask an AI for guidance, democracy is mediated by accurate context rather than silence or distortion.</p>

Political communication is colliding with a new information layer. Voters now ask ChatGPT, Gemini or Google’s AI Mode to explain what parties stand for, who seems credible and how policy trade offs compare, often before they see a single ad or click a result. Google reports that more than 1.5 billion people have used its AI generated summaries, and generative AI referrals to websites have grown by several thousand percent year on year in sectors like retail, a signal of how quickly behaviour is 

The New Information Layer: Why Political Organizations Must Own Their AI Visibility.

<p>As more people turn to AI for political guidance, perception is no longer formed only by media or social platforms - it’s increasingly shaped by how AI synthesises information from across the internet.&nbsp;</p><p>RankBee helps political campaigns understand and shape how AI systems describe them to voters.&nbsp;</p><p>RankBee reveals what questions voters ask AI, which attributes AI uses to answer them, and how a candidate is currently framed across key issues. RankBee then tests and optimizes content so AI is more likely to trust, cite, and associate the right messages with the campaign.</p><p>Voters are changing how they seek political information. Instead of scrolling through search results or social feeds, many now ask AI systems direct questions: <em>Which candidate aligns with my values? Who has the strongest economic plan? Who can I trust on public safety?</em></p><p>The answers they receive don’t come from campaign slogans or party manifestos. They are constructed by AI systems that scan the internet, weigh credibility, identify patterns, and synthesise what they believe matters most. This shift has created a new strategic layer for modern campaigns - one that operates quietly, but at massive scale.</p><p>This is where <strong>SEP for AI - Search, Exposure, and Persuasion for AI systems -</strong> becomes critical.</p><p>AI does not support candidates or take political positions. Instead, it interprets questions through attributes: the underlying factors voters care about when making decisions. Economic competence, integrity, leadership experience, policy clarity, and consistency across sources all play a role in how AI frames political answers. When someone asks who aligns with their views, AI isn’t choosing a winner - it’s matching attributes to available evidence.</p><p>Campaigns that understand this distinction gain a powerful advantage.</p><p>Rather than optimizing for slogans or headlines, SEP for AI focuses on attribute ownership. It examines the real questions people ask AI, identifies how those questions are broken down internally, and reveals which traits AI associates with each candidate. From there, campaigns can see how they are framed across different issues, where narratives are strong, and where perception is weak or undefined.</p><p>The real shift comes when messaging moves from intuition to proof. With SEP for AI, campaign content can be tested and simulated before it’s published. Different versions of the same message can be evaluated to see which one AI is more likely to cite, trust, and use when forming answers. Instead of guessing what will resonate, campaigns can make evidence‑based decisions about how their policies, track record, and priorities are communicated.</p><p>This isn’t about manipulating AI. It’s about clarity, consistency, and credibility across the sources AI relies on - from official websites and press releases to interviews and explanatory content. When those signals align, AI reflects them back to voters.</p><p>Traditional political strategy already accounts for polling, media coverage, and public sentiment. SEP for AI adds a new dimension: understanding how AI explains a candidate to undecided voters at the exact moment they’re seeking guidance.</p><p>Campaigns persuade people - RankBee shapes what AI says when people ask.</p><p>In an era where AI increasingly mediates political understanding, that distinction may prove decisive.</p><p>Aris Vrakas, The Founder and CEO of RankBee, is a speaker at “Politics Meets Technology" on 24 January 2026 in Berlin.  &nbsp;<br><br></p>

As more people turn to AI for political guidance, perception is no longer formed only by media or social platforms - it’s increasingly shaped by how AI synthesises information from across the internet. 

RankBee helps political campaigns understand and shape how AI systems describe them to voters. 

RankBee reveals what questions voters ask AI, which attributes AI uses to answer them, and how a candidate is currently framed across key issues. RankBee then tests and optimizes content so AI is mor

AI Search: The New Invisible Battleground in Political Campaigns

<p><strong>AI visibility is the measurable presence of a brand inside AI-generated answers</strong>, including mentions, citations, accuracy, and category framing. This guide offers a beginner friendly overview about what AI visibility is and how AI search works, what metrics matter, and how measurement happens in practice. It also connects AI visibility measurement with LLM optimization, exploring remediation paths for low visibility on AI powered search interfaces.</p><div class="kg-card kg-button-card kg-align-left"><a href="https://rankbee.ai/demo?ref=geo.rankbee.ai" class="kg-btn kg-btn-accent">Book a discovery call to improve your AI visibility</a></div><h2 id="what-is-ai-visibility">What is AI visibility?</h2><p><strong>AI visibility is a measurement of how often, where, and how correctly a brand appears in AI-generated answers</strong>, including whether the brand is mentioned, cited, and represented with accurate attributes across a defined prompt set. AI visibility contains 5 components: mention rate, citation rate, citation quality, sentiment/stance, and topical coverage. These components describe presence in “AI answers” and “generated answers,” not blue-link rankings.</p><h2 id="why-does-ai-visibility-matter-in-2026">Why does AI visibility matter in 2026?</h2><p>AI visibility matters because answers from LLMs compress discovery into short summaries and recommendations, which moves demand toward brands that appear as sources, cited references, or named options. Low AI visibility removes a brand from AI assisted consideration. AI search visibility affects 3 stages: </p><ul><li>Discovery (“best X”), </li><li>Evaluation (“X vs Y”), and </li><li>Trust (“is X reliable”). </li></ul><p>Search behavior includes AI Overviews, conversational assistants, and answer engines where citations and trust signals shape inclusion. </p><h2 id="how-does-ai-search-surface-brands">How does AI search surface brands?</h2><p><strong>AI search surfaces brands by retrieving passages that match intent, entities, and attributes</strong>, then synthesizing a response that favors clarity, topical authority, and trust signals. Brand inclusion tracks what the system reads and reuses. AI systems follow 3 steps: </p><ul><li>retrieval (source selection), </li><li>synthesis (summary generation), and </li><li>attribution (citations or references). </li></ul><p>Brand presence changes when the prompt changes, because “real user prompts” vary across the funnel.</p><h2 id="what-is-query-fan-out-in-ai-answers">What is query fan-out in AI answers?</h2><p><strong>Query fan-out is prompt expansion where the AI generates related sub-questions to gather context</strong>, which expands retrieval beyond the original query. <u>Query fan-outs determine which subtopics and entities enter the LLM answer</u>. Fan-outs create predictable coverage needs: comparisons, definitions, constraints, edge cases, and examples. A brand is more likely to appear more often when content covers fan-out paths with explicit entities, quotable claims, and updated facts. </p><h2 id="how-do-you-measure-ai-visibility">How do you measure AI visibility?</h2><p><strong>AI visibility measurement starts with a prompt library reflecting your brand's semantic space, tests across AI platforms, and logs outputs over time</strong>, including mentions, citations, and correctness. Baselines convert observations into trends and deltas. <u>Rankbee is the only AI visibility platform that scores content at the attribute level</u>. In this context, attributes are the features and benefits AI models actually reason about for a given user intent. </p><blockquote class="kg-blockquote-alt">Forget keywords. Optimize for attributes.</blockquote><p>For example, <strong>'responsible gambling'</strong> (understood as 'set of practices designed to ensure that gambling remains an&nbsp;enjoyable form of entertainment&nbsp;rather than a financial or emotional burden') <strong>is a critical attribute for a business offering sport betting</strong>. </p><p>Or '<strong>Policy positions and priorities'</strong> (understood as <em>the set of stances and issue areas a candidate emphasizes and consistently communicates</em>) are a critical attribute for <a href="https://geo.rankbee.ai/llm-optimization-for-political-campaigns/" rel="noreferrer">political campaigns optimizing AI visibility</a>.</p><p>A complete Rankbee workflow to measure AI visibility uses 5 layers: </p><ul><li>Category, sub-category and attribute definition.</li><li>Prompt set by ICP and intent.</li><li>Repeated runs across AI engines.</li><li>Extraction of mentions/citations.</li><li>Reporting by segment, competitor and citation sources. </li></ul><h2 id="how-do-ai-visibility-tools-work">How do AI visibility tools work?</h2><p><strong>AI visibility tools automate prompt generation and tracking, normalize outputs across models, and extract brand signals</strong>, <strong>including mentions and citations.</strong> AI visibility platforms then compute share-of-voice and trend reporting. </p><blockquote>Rankbee finds what data LLMs consider important, which sources they trust and what assumptions shape their answers.</blockquote><p>Most platforms include 5 modules: prompt management, multi-engine testing, parsing/extraction, scoring/benchmarking, and dashboards. Tooling differs on model coverage, citation parsing quality, export formats, and integrations with other tools. </p><h2 id="how-do-you-choose-an-ai-visibility-tool">How do you choose an AI visibility tool?</h2><p>The most important factors to select an AI visibility tracking tool are <u>brands' semantic space creation, prompt-run consistency, AI engine coverage, and extraction accuracy for citations and mentions</u>. Additional features to consider are <u>metric transparency, exports, and integrations</u>. Depending on your business, you may also want to consider other features. </p><p>For example, marketing agencies care about client workspaces, ability to track brands in multiple countries and languages, white label reporting, LLM content optimization features. Enterprises care about access control, multi-brand tracking, audit logs, custom setups and ad-hoc consulting &amp; reporting. </p><h2 id="how-is-ai-visibility-different-from-seo-visibility">How is AI visibility different from SEO visibility?</h2><p><strong>AI visibility measures inclusion inside generated answers; traditional SEO measures visibility in ranked results and clicks.</strong> </p><ul><li>AI visibility emphasizes citations, synthesis inclusion, entity accuracy, and share-of-voice across prompts. </li><li>SEO emphasizes rankings, CTR, and sessions. Traditional SEO evaluates pages; AI visibility evaluates answers built from multiple sources. These differences explain “LLM optimization vs traditional SEO.”</li></ul><h2 id="what-is-an-ai-visibility-strategy">What is an AI visibility strategy?</h2><p><strong>An AI visibility strategy aligns business outcomes to prompt sets, content priorities, and testing cadence</strong>, then iterates using measurement. An strategy to improve your brand's AI visibility connects SEO, content, PR, and product narratives into one answer-engine plan. You can use Rankbee to identify relevant prompts for your ideal customers, benchmark your brand coverage, identify your competitor set and most influential citation targets. Finally you will also be able to create new content and/or optimize existing content on your website. </p><h2 id="how-do-you-fix-low-ai-visibility">How do you fix low AI visibility?</h2><p>In order to increase your brand's AI visibility on LLMs, you must first understand your baseline visibility for every category, sub-category, and attribute that are representative of your business. At that point, you can start optimizing different aspects of your brand presence, such as:</p><ul><li>Publishing targeted content that matches fan-out sub-questions and attributes.</li><li>Ensuring your brand is recognized as a well-known entity.</li><li>Refreshing content regularly and correcting stale facts.</li><li>Earning citations to increase trust signals.</li></ul><p>Rankbee helps brands understand AI visibility, measure it across AI search and LLMs, and define strategies to grow their presence in AI-powered search.</p><div class="kg-card kg-button-card kg-align-left"><a href="https://rankbee.ai/demo?ref=geo.rankbee.ai" class="kg-btn kg-btn-accent">Book a discovery call to improve your AI visibility</a></div>

AI visibility is the measurable presence of a brand inside AI-generated answers, including mentions, citations, accuracy, and category framing. This guide offers a beginner friendly overview about what AI visibility is and how AI search works, what metrics matter, and how measurement happens in practice. It also connects AI visibility measurement with LLM optimization, exploring remediation paths for low visibility on AI powered search interfaces.

Book a discovery call to improve your AI visibi

AI Visibility Guide: Tracking and Optimizing Brand Presence in LLMs (2026)

<p>Large language models (LLMs) like ChatGPT and AI search interfaces like Google AI Overviews are becoming a “new front page” for voter research. Voters don’t just search; they <em>ask</em> questions like “Which candidate is better for the economy?” or “Who will lower my taxes?”. </p><blockquote class="kg-blockquote-alt">Undecided voters decide elections. Be present where AI shapes opinions.</blockquote><p>When those questions get answered by AI, <strong>your campaign’s narrative is either represented accurately, partially, or not at all</strong>.</p><figure class="kg-card kg-image-card kg-card-hascaption"><img src="https://geo.rankbee.ai/content/images/2025/12/AI-visibility-optimization-for-political-campaigns-8.png" class="kg-image" alt="AI visibility optimization for political campaigns" loading="lazy" width="634" height="350" srcset="https://geo.rankbee.ai/content/images/size/w600/2025/12/AI-visibility-optimization-for-political-campaigns-8.png 600w, https://geo.rankbee.ai/content/images/2025/12/AI-visibility-optimization-for-political-campaigns-8.png 634w"><figcaption><span style="white-space: pre-wrap;">AI visibility optimization for political campaigns</span></figcaption></figure><p><strong>LLM optimization for political campaigns</strong> <strong>measures how AI systems describe your candidate, party, and issue positions, and tries to improve the factual base and structured content that AI uses to generate those answers</strong>—so your campaign is reflected credibly, comprehensively, and consistently. </p><div class="kg-card kg-button-card kg-align-left"><a href="https://rankbee.ai/demo?ref=geo.rankbee.ai" class="kg-btn kg-btn-accent">Book a discovery call to improve your AI visibility</a></div><hr><h2 id="why-ai-visibility-measurement-is-step-one">Why AI visibility measurement is step one?</h2><p>AI visibility is your campaign’s <em>share of representation</em> inside AI-generated answers. In practice, it’s whether (and how) your campaign appears when voters ask. <u>If you’re not measuring this, you’re flying blind—because AI answers can shift quickly as news cycles, sources, and online narratives change</u>.</p><p><strong>Measurement matters because it reveals:</strong></p><ul><li>Which voter questions you’re winning vs. losing (visibility coverage)</li><li>Which sources AI trusts about you (source dependence)</li><li>Which tiny factual claims are driving perception—accurate or not</li><li>Where misinformation or missing context is causing damaging summaries</li></ul><p>Think of it like polling, but for <strong>AI-generated voter impressions</strong>.</p><blockquote>Rankbee helps you transform “what AI says” from a mystery into a measurable, improvable campaign asset.</blockquote><hr><h2 id="how-ai-forms-opinions-about-candidates-and-parties">How AI forms opinions about candidates and parties</h2><p>AI systems try to match a voter’s prompt to a “question” and then assemble an “answer” from the most available, trusted, and structured information. That matters because <u>campaigns often publish plenty of content, but not in a format AI can confidently use</u>. AI is biased toward content that is:</p><ul><li><strong>Directly answering a question</strong></li><li><strong>Consistent in entity naming</strong> (candidate name, office, district, party)</li><li><strong>Supported by primary and reputable sources</strong></li><li><strong>Structured</strong> (clear headings, bullet lists, definitions, comparisons)</li></ul><p>When that structure is missing, AI tends to fill gaps with incomplete summaries, third-party narratives you didn’t shape, or “averaged” interpretations that understate your strengths.</p><p>This is why LLM optimization begins with <strong>understanding what AI considers important, what sources it trusts, and what assumptions shape its answers</strong>.</p><hr><h2 id="the-real-ai-%E2%80%9Cbattlefield%E2%80%9D-the-questions-that-matter-to-undecided-voters">The real AI “battlefield”: the questions that matter to undecided voters</h2><p>Campaigns usually think in messaging pillars. Voters think in <strong>prompts</strong>. Below is a prompt taxonomy to shed some light about the different user intents that voters ask:</p><h3 id="1-candidate-perception-prompts">1) Candidate perception prompts</h3><p>These are reputation + trust prompts:</p><ul><li>“What do you think about <strong>Candidate X</strong>?”</li><li>“Is Candidate X moderate or extreme?”</li><li>“What are Candidate X’s biggest accomplishments?”</li><li>“What controversies are associated with Candidate X?”</li><li>“Is Candidate X trustworthy / corrupt / competent?”</li></ul><h3 id="2-political-party-perception-prompts">2) Political party perception prompts</h3><ul><li>“What does <strong>[Party]</strong> stand for?”</li><li>“What kind of voters support [Party]?”</li><li>“What are the main factions inside [Party]?”</li><li>“Is [Party] good for the economy?”</li></ul><h3 id="3-partycandidate-stance-prompts-by-issue">3) Party/candidate stance prompts by issue</h3><p>Issue-specific prompts are the highest volume in most elections:</p><ul><li>Economy: “Which candidate is better for the economy?”</li><li>Taxes: “Who will lower my taxes?”</li><li>Immigration: “What is Candidate X’s immigration plan?”</li><li>Education: “What’s this mayor’s record on education?”</li><li>Healthcare: “Will Candidate X expand coverage or cut costs?”</li></ul><h3 id="4-positivenegative-prompts-handle-ethically">4) Positive/negative prompts (handle ethically)</h3><p>Voters ask these explicitly. Your job is to answer them <em>credibly</em>.</p><ul><li>“What are the strongest arguments for Candidate A?”</li><li>“What are the biggest criticisms of Candidate B?”</li><li>“Is it true that Candidate X did Y?”</li></ul><h3 id="5-candidate-a-vs-candidate-b-prompts">5) Candidate A vs Candidate B prompts</h3><p>Comparative prompts create “winner/loser” framing:</p><ul><li>“Candidate A vs Candidate B on taxes”</li><li>“Who supports small businesses more?”</li><li>“Which candidate has a better record on education?”</li></ul><hr><h2 id="llm-optimization-workflow-for-political-campaigns">LLM optimization workflow for political campaigns</h2><p>A practical LLM optimization program can be run as a monthly cycle (and weekly during high-volatility periods). A proven structure includes:</p><ol><li><strong>AI Topic Simulation</strong></li><li><strong>Source &amp; Attribute Mapping</strong></li><li><strong>Narrative Reinforcement</strong></li><li><strong>AI-Optimised Content Creation</strong></li></ol><h3 id="step-1-ai-topic-simulation-find-the-questions-and-the-%E2%80%9Clogic%E2%80%9D-ai-uses">Step 1: AI Topic Simulation (find the questions and the “logic” AI uses)</h3><p>Rankbee simulates hundreds of voter-like conversations to uncover what data AI considers important, which sources it trusts and what assumptions shape its answers.</p><p><strong>Campaign value:</strong> you stop guessing what “matters” and start optimizing for the real intents and “high-risk” prompts (e.g. misinfo-prone, opposition-framed),</p><h3 id="step-2-source-attribute-mapping-identify-the-%E2%80%9Cmicro-truths%E2%80%9D-driving-perception">Step 2: Source &amp; Attribute Mapping (identify the “micro-truths” driving perception)</h3><p>Rankbee identifies every “micro-truth” influencing AI output—from news coverage and public records to past speeches and economic data.</p><p><strong>Campaign value:</strong> you discover exactly <em>which</em> facts and references AI is using—so you can strengthen what’s accurate and address what’s wrong.</p><p><strong>Outputs you want:</strong></p><ul><li>top sources AI pulls from (earned + owned + third-party),</li><li>missing attributes (e.g., AI can’t find your position on small business grants),</li><li>conflicting sources (where AI sees mixed signals).</li></ul><h3 id="step-3-narrative-reinforcement-make-the-accurate-story-easier-to-retrieve">Step 3: Narrative Reinforcement (make the accurate story easier to retrieve)</h3><p>This step focuses on:</p><ul><li>strengthening and quoting authoritative sources aligned with your message,</li><li>introducing or updating facts where misinformation exists,</li><li>ensuring key talking points appear across influential sources.</li></ul><p><strong>Campaign value:</strong> you’re not “gaming” AI—you’re improving the availability and clarity of verifiable information so AI has less reason to hallucinate or rely on hostile framing.</p><h3 id="step-4-optimised-content-creation">Step 4: Optimised Content Creation</h3><p>Campaign materials—speeches, blogs, press releases—are rewritten and structured so AI interprets them as credible, comprehensive, and contextually relevant.</p><p><strong>Campaign value:</strong> your best messaging becomes “AI-legible,” which increases the likelihood it’s summarized accurately and cited.</p><hr><h2 id="a-concrete-example-%E2%80%9Cwhich-candidate-helps-small-businesses-the-most%E2%80%9D">A concrete example: “Which candidate helps small businesses the most?”</h2><p>This is a perfect “AI battleground” query because it’s comparative, values-driven, and fact-dependent. A strong LLM optimization flow looks like this:</p><ol><li><strong>AI simulation reveals the factual factors AI checks</strong> (the example notes 20 factors such as tax policy, grants, and regulation record).</li><li><strong>Map which sources AI uses</strong> to form its view.</li><li><strong>Update campaign content and reinforce supportive third-party articles</strong> so the best evidence is available.</li><li>Over time, AI responses may begin <strong>citing your candidate’s record and proposals</strong> more directly.</li></ol><blockquote>You’re not optimizing for a single page. Rankbee helps you to optimize the <em>evidence and structure</em> that AI assembles into an answer.</blockquote><hr><h2 id="a-practical-30%E2%80%9360%E2%80%9390-day-plan-for-campaign-managers">A practical 30–60–90 day plan for campaign managers</h2><h3 id="days-1%E2%80%9330-establish-baseline-and-fix-the-biggest-gaps">Days 1–30: Establish baseline and fix the biggest gaps</h3><ul><li>Build your prompt library (perception, issue, comparison, claim-check)</li><li>Run AI topic simulation to find what AI “checks” and which sources it trusts</li><li>Identify 10–20 “high-impact prompts” that shape persuasion</li><li>Publish your one-page hub with:<ul><li>fact set block,</li><li>5–7 issue modules,</li><li>FAQ for top prompts</li></ul></li></ul><p><strong>Success looks like:</strong> you can point to what AI currently says, where it comes from, and what’s missing.</p><h3 id="days-31%E2%80%9360-reinforce-narrative-with-sources-and-structure">Days 31–60: Reinforce narrative with sources and structure</h3><ul><li>Perform source &amp; attribute mapping to identify micro-truth gaps</li><li>Strengthen authoritative citations and update misinformation-prone sections</li><li>Rewrite speeches/press releases into “AI-optimized” formats (same meaning, better structure)</li></ul><p><strong>Success looks like:</strong> AI answers become more consistent, and citations shift toward stronger sources.</p><h3 id="days-61%E2%80%9390-expand-coverage-and-reduce-volatility">Days 61–90: Expand coverage and reduce volatility</h3><ul><li>Add deeper FAQs for comparison prompts</li><li>Add claim-check entries for recurring attacks</li><li>Re-run simulations monthly (or weekly in late-cycle)</li><li>Report on coverage, accuracy, source share, volatility</li></ul><p><strong>Success looks like:</strong> fewer surprise AI answers, fewer missing/incorrect claims, and stronger “share of representation” in decisive topics.</p><hr><h2 id="ethics-and-compliance-how-to-do-this-the-right-way">Ethics and compliance: how to do this the right way</h2><p>Political content is high-stakes. The safest (and most durable) strategy is to optimize for <strong>accuracy, verifiability, transparent sourcing, </strong>and clear distinctions between <em>fact</em>, <em>proposal</em>, and <em>opinion</em>.</p><p>The workflow explicitly emphasizes facts, trust signals, and authoritative sources.<br>That should guide your implementation: publish what you can prove, correct what’s wrong, and reduce ambiguity so AI doesn’t “fill in the blanks.”</p><hr><h2 id="how-rankbee-can-help-political-campaigns">How Rankbee can help political campaigns</h2><p>Campaign managers are busy. The value that Rankbee offers is speed, experience and repeatability. The following are some of the benefits:</p><ul><li>Always-on monitoring of high-impact prompts</li><li>Automated alerting when answers drift (volatility)</li><li>Source mapping so you know what to fix first</li><li>A content optimization engine that restructures materials for AI comprehension</li><li>Strategic guidance on narrative reinforcement across trusted sources</li></ul><div class="kg-card kg-button-card kg-align-left"><a href="https://rankbee.ai/demo?ref=geo.rankbee.ai" class="kg-btn kg-btn-accent">Book a discovery call to improve your AI visibility</a></div>

Large language models (LLMs) like ChatGPT and AI search interfaces like Google AI Overviews are becoming a “new front page” for voter research. Voters don’t just search; they ask questions like “Which candidate is better for the economy?” or “Who will lower my taxes?”.

Undecided voters decide elections. Be present where AI shapes opinions.

When those questions get answered by AI, your campaign’s narrative is either represented accurately, partially, or not at all.

LLM optimization for politic

LLM Optimization for Political Campaigns

<p><a href="https://www.linkedin.com/in/dforrester/?ref=geo.rankbee.ai">Duane Forrester</a>'s article <a href="https://lnkd.in/dw8jnVVp?ref=geo.rankbee.ai">https://lnkd.in/dw8jnVVp</a> featuring RankBee is a timely wake‑up call: traditional ranking metrics are no longer sufficient in a world dominated by generative Ai search engines. It highlights the new SEO reality - to win in ChatGPT and Gemini, brands need to optimise for how LLMs process data.</p><p>Duane argues that SEO training is stuck in the past, still focused on title tags, link building, and keyword targeting, while the reality of search has shifted to generative Ai systems like ChatGPT and Gemini. These models don’t rank pages—they assemble answers from semantically rich, structured, and trustworthy content that can be easily retrieved and cited. Duane lays out how SEO professionals must adapt by learning new practices such as semantic chunking, embedding alignment, trust-signal engineering, and retrieval simulation. It highlights why the disconnect persists—educational inertia, outdated hiring, and legacy tools—and proposes a roadmap for practitioners to self-educate, run retrieval audits, and establish AI-SEO leadership within their organizations. </p>

Duane Forrester's article https://lnkd.in/dw8jnVVp featuring RankBee is a timely wake‑up call: traditional ranking metrics are no longer sufficient in a world dominated by generative Ai search engines. It highlights the new SEO reality - to win in ChatGPT and Gemini, brands need to optimise for how LLMs process data.

Duane argues that SEO training is stuck in the past, still focused on title tags, link building, and keyword targeting, while the reality of search has shifted to generative Ai sys

Is the SEO industry teaching the wrong skills?

<p>Claude prompt leak changes how you earn AI citations </p><figure class="kg-card kg-image-card"><img src="https://geo.rankbee.ai/content/images/2025/09/image-2.png" class="kg-image" alt="" loading="lazy" width="1830" height="1090" srcset="https://geo.rankbee.ai/content/images/size/w600/2025/09/image-2.png 600w, https://geo.rankbee.ai/content/images/size/w1000/2025/09/image-2.png 1000w, https://geo.rankbee.ai/content/images/size/w1600/2025/09/image-2.png 1600w, https://geo.rankbee.ai/content/images/2025/09/image-2.png 1830w" sizes="(min-width: 720px) 720px"></figure><p>If you chase rankings alone, you’ll miss where AI actually sends clicks. The recently analyzed Claude 4 system‑prompt shows when the model triggers a live web search and when it will attach a clickable citation – a playbook you can optimize for. (Source:&nbsp;<a href="https://gpt-insights.de/ai-insights/gpt-insights-claude-leak-en/?ref=geo.rankbee.ai">GPT Insights</a>)</p><p>Anthropic has also been publishing official “system prompt” release notes for Claude’s apps since 2024, confirming that default prompts guide tool use and behavior, although the API isn’t affected. Treat any leaked text as indicative, not canonical. (Source:&nbsp;<a href="https://docs.anthropic.com/en/release-notes/system-prompts?ref=geo.rankbee.ai">Anthropic Docs</a>)</p><h2 id="why-this-matters-now">Why this matters now</h2><p>Start with pages that AI already cites – they refresh fast. When a page that’s frequently used in AI answers changes, the updated details tend to appear in new citations soon after, because the model fetches fresh results during live search.</p><blockquote>At RankBee.ai we see&nbsp;<strong>~25% of prompts invoke ChatGPT’s web search</strong>&nbsp;in real usage. Scope: anonymized, English prompts across mixed consumer and B2B intents, April–June 2025. Limitation: vendor telemetry, not a platform‑wide audit. Prioritize pages with existing AI citations to move the needle first.</blockquote><h2 id="what-the-claude-leak-signals-about-linking-behavior">What the Claude leak signals about linking behavior</h2><p>Claude is instructed to link when the target page provides something the model shouldn’t trivialize:</p><ul><li>Interactive tools – outputs tailored per user, e.g., mortgage payoff calculators or tyre‑size matchers.</li><li>Continuously updated datasets – real‑time airfare trackers, commodity dashboards, election tallies.</li><li>Unique user reviews &amp; ratings – subjective and fast‑changing, safer to reference than restate at length.</li><li>Regional or niche reports – time‑bound, place‑specific briefs where generic text fails.</li><li>Expert analysis – original commentary with charts or methods that carry interpretation, not just facts. (Source:&nbsp;<a href="https://gpt-insights.de/ai-insights/gpt-insights-claude-leak-en/?ref=geo.rankbee.ai">GPT Insights</a>)</li></ul><h2 id="deep-dive-the-ai-citation-optimization-playbook">Deep Dive: The AI citation optimization playbook</h2><p><strong>Objective</strong>: increase the odds of being linked – and clicked – from AI answers by aligning pages to known citation triggers.</p><ol><li><strong>Inputs</strong>: </li></ol><p>Collect list of pages receiving AI citations today. Use RankBee analytics and AI‑referred sessions from your analytics to help you discovered your Ai cited pages. </p><ol start="2"><li><strong>Design for link‑worthiness</strong></li></ol><ul><li>Inputs: top 20 candidate pages with historic citations.</li><li>Activities:</li></ul><blockquote>Rewrite Content using the RankBee Content Optimiser, which augments pages to increase the number of citations, as well as the number words used to talk about your brand.</blockquote><ul><ul><li>Add an interactive element where relevant – calculator, configurator, live map.</li><li>Expose machine‑readable facts: JSON‑LD with&nbsp;<strong>schema.org/Dataset</strong>,&nbsp;<strong>Review</strong>,&nbsp;<strong>Product</strong>,&nbsp;<strong>FAQPage</strong>.</li><li>Add a compact “copy block” with the one line an LLM can quote safely.</li></ul></ul><ol start="3"><li><strong>Measure and repeat</strong></li></ol><ul><li>Inputs: GSC, analytics, RankBee.ai citation reports.</li><li>Activities:<ul><li>Track AI‑referred traffic, CTR from chat UIs when available, assisted conversions.</li><li>Compare time‑to‑impact for edited cited pages vs net‑new pages.</li></ul></li><li>Outputs: ROI model on AI citations with page‑level lift. Typically RankBee optimised content increases the number of citations within 2 weeks. </li></ul><ol start="4"><li><strong>30/60/90 adoption</strong></li></ol><ul><li>30: Cited‑page inventory, optimize content for top 10 pages</li><li>60: Optimize content for 50% pages and add interactive modules to 3–5 high‑intent pages, automate two feeds</li><li>90: Optimize all product and article pages. </li></ul><h2 id="proof-points-you-can-cite-internally">Proof points you can cite internally</h2><ul><li>Anthropic publicly logs system‑prompt changes for app experiences – evidence that prompts govern tool use and style. (Source:&nbsp;<a href="https://docs.anthropic.com/en/release-notes/system-prompts?ref=geo.rankbee.ai">Anthropic Docs</a>)</li><li>Industry coverage confirms Anthropic began publishing system‑prompt details in 2024 for transparency. (Source:&nbsp;<a href="https://techcrunch.com/2024/08/26/anthropic-publishes-the-system-prompt-that-makes-claude-tick/?ref=geo.rankbee.ai">TechCrunch</a>)</li></ul><h2 id="caveats">Caveats</h2><p>What this is not: A guarantee that authority signals or brand size alone earn links. Claude’s prompt prioritizes fit to the user query and recency over generic E‑A‑T claims. (Source:&nbsp;<a href="https://gpt-insights.de/ai-insights/gpt-insights-claude-leak-en/?ref=geo.rankbee.ai">GPT Insights</a>)</p><p>This summary is based on leaked prompt text reviewed by GPT Insights, and cross-checked with Anthropic’s public system-prompt notes for context.</p><p>Stance: Claude includes links only when the answer depends on fresh information, personalisation information relating to the user and content that cannot be paraphrased. It does <em>not</em> link just because a page ranks highly. (Source:&nbsp;<a href="https://gpt-insights.de/ai-insights/gpt-insights-claude-leak-en/?ref=geo.rankbee.ai">GPT Insights</a>)</p><p>Limitations: Anthropic’s public notes don’t confirm every leaked instruction. Treat these as best‑fit heuristics, not hard guarantees. (Source:&nbsp;<a href="https://docs.anthropic.com/en/release-notes/system-prompts?ref=geo.rankbee.ai">Anthropic Docs</a>)</p><p></p><h3 id=""></h3><p>Citations: </p><figure class="kg-card kg-bookmark-card"><a class="kg-bookmark-container" href="https://gpt-insights.de/ai-insights/gpt-insights-claude-leak-en/?ref=geo.rankbee.ai"><div class="kg-bookmark-content"><div class="kg-bookmark-title">Claude Leak: How LLMs Search – What SEOs Need to Know</div><div class="kg-bookmark-description">The Claude leak reveals for the first time how an LLM like Claude 4 decides whether to search, link to content, or answer from internal knowledge – with major implications for SEO.</div><div class="kg-bookmark-metadata"><img class="kg-bookmark-icon" src="https://geo.rankbee.ai/content/images/icon/gpt-insight-logo-black-box-200.png" alt=""><span class="kg-bookmark-author">GPT Insights</span><span class="kg-bookmark-publisher">Hanns Kronenberg</span></div></div><div class="kg-bookmark-thumbnail"><img src="https://geo.rankbee.ai/content/images/thumbnail/claude-leak-en.png" alt="" onerror="this.style.display = 'none'"></div></a></figure>

Claude prompt leak changes how you earn AI citations

If you chase rankings alone, you’ll miss where AI actually sends clicks. The recently analyzed Claude 4 system‑prompt shows when the model triggers a live web search and when it will attach a clickable citation – a playbook you can optimize for. (Source: GPT Insights)

Anthropic has also been publishing official “system prompt” release notes for Claude’s apps since 2024, confirming that default prompts guide tool use and behavior, although th

Claude prompt leak changes how you earn AI citations

<p>Google Search Just Changed - Here’s What It Means for You.</p><p>Google has rolled out a major update to its AI Overviews in the US, now renamed “AI Mode”, marking a new phase of the AI platform shift.&nbsp;This experience will automatically activate when Google detects complex queries. </p><p>Instead of ten blue links, users now get a synthesised AI-generated response - with cited sources and actions like "Add to Sheets" - right at the top of the results page. </p><p>The default experience for many searches is no longer traditional search.&nbsp; About 1.5 billion people now regularly engage with “AI Overviews”, according to Google, and most users are now entering longer and more complex queries.</p><p>It’s AI-first, and it’s changing how people discover, compare, and decide. </p><p>For brands, this means:</p><p>🔍 Your web page might not be clicked, even if it’s cited.<br>🔍 Product comparisons and reviews could be summarised by AI - not by your content.<br>🔍 The visibility you worked hard for in classic SEO may not translate to visibility in AI Overviews.What should you do about it?At RankBee, we’re tracking and analysing how AI models like Gemini and ChatGPT mention and recommend brands - because that’s where customer attention is going. </p><p>Here’s what we recommend:</p><p>💡 Audit how your brand appears in AI Overviews and LLM-generated content.<br>💡 Optimise for generative engines, not just search engines.<br>💡 Track attribution loss: Are you getting cited but not clicked?<br>💡 Invest in brand authority and structured content that LLMs can trust and reuse.</p><p>🐝 How often is your brand part of the conversation, but not part of the click? </p>

Google just replaced “AI Overviews” with AI Mode — and for many searches, it’s now the default experience. Instead of ten blue links, users see an AI-generated answer with sources and actions. For brands, that means visibility doesn’t guarantee clicks. Here’s what it means for your strategy.

Google Search Is sometimes Ai-First — What That Means for Your Brand

<p>When customers search with AI tools like ChatGPT, Gemini, or Perplexity, they don’t just type a keyword — they ask questions in natural language. Instead of “best watches 2025,” a query might look like: </p><blockquote><em>“What’s a reliable, comfortable watch under $500 that’s accurate and water-resistant?”</em></blockquote><p>This shift means that product <strong>attributes</strong> — the qualities, features, and specifications that define your product — are now central to visibility in AI-driven search.</p><h2 id="why-attributes-are-critical-in-ai-search">Why Attributes Are Critical in AI Search</h2><p>AI doesn’t scan the web looking for keyword matches. Instead, it tries to understand <em>context</em> and <em>meaning</em>. Attributes act as the building blocks of that understanding. They help AI models decide:</p><ul><li>Whether your product fits the intent of a customer’s query.</li><li>How your product compares to alternatives.</li><li>What details are important enough to summarize in an AI-generated answer.</li></ul><p>For example, in the watch category, attributes like:</p><ul><li><strong>Brand reputation</strong> (trustworthiness, recognition)</li><li><strong>Accuracy</strong> (mechanical vs. quartz, precision ratings)</li><li><strong>Comfort</strong> (strap material, weight, fit)</li><li><strong>Water resistance</strong> (ATM rating, use cases like swimming or diving)</li></ul><p>…can make the difference between being included in the AI’s response or being ignored.</p><h2 id="what-happens-if-attributes-are-missing">What Happens if Attributes Are Missing?</h2><p>If your content doesn’t explicitly describe these attributes, the AI may:</p><ul><li>Infer them from competitor listings.</li><li>Leave your product out of the generated summary.</li><li>Reduce your visibility in “AI Overviews” and conversational search.</li></ul><p>Put simply: if the AI can’t <em>see</em> the attribute, your product doesn’t <em>exist</em> for that type of query.</p><h2 id="how-rankbee-helps">How RankBee Helps</h2><p>At RankBee, we analyze how AI systems interpret your product content. We:</p><ol><li><strong>Identify the attributes AI considers most relevant</strong> in your category.</li><li><strong>Audit your content</strong> to spot gaps where attributes are missing or unclear.</li><li><strong>Highlight opportunities</strong> to align your product descriptions and marketing with the attributes AI is prioritizing.</li></ol><p>This insight doesn’t just improve your visibility in AI search. It also gives your marketing and content teams a roadmap: which attributes to emphasize, how to structure content, and where competitors may be outperforming you.</p><h2 id="turning-attributes-into-strategy">Turning Attributes Into Strategy</h2><p>When you write about your products, think beyond keywords. Ask yourself:</p><ul><li>Which attributes matter most to customers in my category?</li><li>Are those attributes clearly described in my product content?</li><li>Does my content provide enough detail for an AI to confidently recommend my product?</li></ul><p>By aligning your content with the right attributes, you increase your chances of being selected, summarized, and recommended by AI assistants — the new discovery engines for billions of users.</p>

When customers search with AI tools like ChatGPT, Gemini, or Perplexity, they don’t just type a keyword — they ask questions in natural language. Instead of “best watches 2025,” a query might look like:

“What’s a reliable, comfortable watch under $500 that’s accurate and water-resistant?”

This shift means that product attributes — the qualities, features, and specifications that define your product — are now central to visibility in AI-driven search.


Why Attributes Are Critical in AI Search


Attributes & AI Search: Why Writing About Product Attributes Matters

<p>OpenAI is in the process of launching (Organic) Product Placement within Answers. <a href="https://lnkd.in/deM9tJ4w?ref=geo.rankbee.ai">https://lnkd.in/deM9tJ4w</a></p><p>To this date, the impact of ChatGPT and AI Agents has been transformational for SAAS, Services and Local Services companies. This latest product release will create a huge opportunity for Retail and E-commerce companies to compete for discovery in ChatGPT. </p><p>Direct to Consumer, Big Retail, used products, all will see a great shift in how their products are discovered by consumers. </p><p><a href="https://www.linkedin.com/company/rankbee/?ref=geo.rankbee.ai">RankBee.ai</a> is already working on a feature that allows customers to analyse if your products will be visible on ChatGPT when they launch this feature.</p><p>More details here: <a href="https://openai.com/chatgpt/search-product-discovery/?ref=geo.rankbee.ai">https://openai.com/chatgpt/search-product-discovery/</a></p>

OpenAI is in the process of launching (Organic) Product Placement within Answers. https://lnkd.in/deM9tJ4w

To this date, the impact of ChatGPT and AI Agents has been transformational for SAAS, Services and Local Services companies. This latest product release will create a huge opportunity for Retail and E-commerce companies to compete for discovery in ChatGPT.

Direct to Consumer, Big Retail, used products, all will see a great shift in how their products are discovered by consumers.

RankBee.

Help ChatGPT discover your products

<p>In today’s digital landscape, understanding where your customers come from is key to optimizing your marketing strategy. One emerging source of inbound leads? AI bots like ChatGPT and Gemini. These tools are increasingly shaping how people discover businesses, and including "AI Bot" as an option in your "Where Did You Hear About Us?" form can reveal their impact on your lead generation. </p><p>By adding "AI Bot" to your form, you can measure how often these platforms drive traffic to your business. This insight helps you refine your marketing efforts and stay ahead in a tech-driven world. Here’s a suggested list of options to include in your form to capture a wide range of lead sources:</p><p>- Search Engine (Google, Bing etc)<br>- Social Media- Online Ads<br>- Ai Bot- Word of Mouth<br>- Email- Blog / Article<br>- TV / Radio<br>- Podcast<br>- Streaming Service Ad<br>- Newspaper</p>

In today’s digital landscape, understanding where your customers come from is key to optimizing your marketing strategy. One emerging source of inbound leads? AI bots like ChatGPT and Gemini. These tools are increasingly shaping how people discover businesses, and including "AI Bot" as an option in your "Where Did You Hear About Us?" form can reveal their impact on your lead generation.

By adding "AI Bot" to your form, you can measure how often these platforms drive traffic to your business. Th

Where did you hear about us?

<p>OpenAI is testing a fully fledged Search Engine powered by ChatGPT: SearchGPT <a href="https://chatgpt.com/search?ref=geo.rankbee.ai">https://chatgpt.com/search</a>.</p><p>The new iteration of the AI powered search engine brings mind blowing new features like.. links to content, images, video, live content and rich graphic experiences. Wow. Sounds like Google 15 years ago – you might think. </p><p>Despite none of this being "new", this is a milestone for OpenAI. The new interface opens up major opportunities and risks. </p><h3 id="is-it-mainstream-yet">Is it mainstream yet?</h3><p>Seems like it's taking forever before it is announced: ChatGPT and Gemini is already used by millions of consumers to make purchase decisions and pick service providers, their holiday destination, or even their car. But we don't really know that yet and there isn't compelling data that shows ChatGPT is used by the mainstream yet. Despite many of us being certain it will be soon.</p><p>Early adopters are certainly using ChatGPT in favour of Google Search for a large portion of their research and search queries, and research has shown that 74% of consumers trust AI assistant recommendations. </p><p>I think we're about to find out soon. One of the side-effects of SearchGPT, is that website analytics tools will start recording HTTP referrer traffic from SearchGPT links. So hopefully we will begin seeing some real data on the number of referrals driven by OpenAI. </p><blockquote>Ultimately, what we marketers want to know is: Is OpenAI mainstream? Can we buy ads yet? How do we optimise AI rankings (LLM SEO?).</blockquote><figure class="kg-card kg-image-card"><img src="https://geo.rankbee.ai/content/images/2025/09/image-1.png" class="kg-image" alt="" loading="lazy" width="1488" height="847" srcset="https://geo.rankbee.ai/content/images/size/w600/2025/09/image-1.png 600w, https://geo.rankbee.ai/content/images/size/w1000/2025/09/image-1.png 1000w, https://geo.rankbee.ai/content/images/2025/09/image-1.png 1488w" sizes="(min-width: 720px) 720px"></figure><p>Apropos, I notice there is a lot of space in the new SearchGPT UI for Paid Search Ads on the left hand side. Don't you think? I would not be surprised if OpenAI is already working behind the scenes with top travel, e-commerce and Digital Ad management tools – testing ad formats and bidding APIs.</p><p>And as far as LLM SEO and brand tracking is concerned - did I mention <a href="https://rankbee.ai/?ref=geo.rankbee.ai">https://rankbee.ai/</a> is soon launching an AI assistant analysis and optimisation tool? Follow RankBee to get early access 😄</p><figure class="kg-card kg-image-card"><img src="https://geo.rankbee.ai/content/images/2025/09/image.png" class="kg-image" alt="" loading="lazy" width="1720" height="396" srcset="https://geo.rankbee.ai/content/images/size/w600/2025/09/image.png 600w, https://geo.rankbee.ai/content/images/size/w1000/2025/09/image.png 1000w, https://geo.rankbee.ai/content/images/size/w1600/2025/09/image.png 1600w, https://geo.rankbee.ai/content/images/2025/09/image.png 1720w" sizes="(min-width: 720px) 720px"></figure><h3 id="what-about-the-new-york-times">What about the New York Times?</h3><p>Well ok this is a topic that peeks my interest – it's about how AI hoovers up all the information that all of us (including companies like the NYT) have painstakenly created, and then uses it to make money for OpenAI and wipes out the creators from the value chain. </p><p>You see, the <a href="https://harvardlawreview.org/blog/2024/04/nyt-v-openai-the-timess-about-face/?ref=geo.rankbee.ai#:~:text=Ultimately%2C%20the%20Supreme%20Court%20found,have%20infringed%20the%20freelancers%27%20copyrights.">NY Times is pissed off with ChatGPT and is suing</a> them – because when ChatGPT users ask questions like, "why did Sam Altman face a board of directors revolt?" – ChatGPT is allegedly using verbatim quotes from the NY Times, without any attribution. Hence arguably using their content to build a (paid) service with their customers and API users – without paying the NY Times for it.</p><p>By providing links to external sites through SearchGPT, OpenAI is defusing a brewing storm from publishers like the New York Times and top retailers like Amazon, who have opposed direct indexation from OpenAI. </p><p>You see the NY Times and Amazon, spent years building direct relationships with their customers. NYT readers get their news from their app through their subscription, and Amazon Prime subscribers, shop directly from Amazon.</p><pre><code>Amazon.com/robots.txt &amp; NYtimes.com/robots.txt: 
User-agent: GPTBot
Disallow: /</code></pre><p>ChatGPT is a threat to publishers and category leaders in retail, travel etc – because if an AI assistant can successfully fulfil customers desires directly, without the consumer visiting the brand's website for the majority of their journey, the consumer relationship shifts. NYT, Amazon and Expedia become a commodity, and ChatGPT becomes the relationship owner with the customer. Example: "buy me a toaster that fits our kitchen", or "Read me my morning news" or "give me some options for an easter school-break holiday". </p><p>In any number of these queries, ChatGPT can shop retailers and content sources, and decide for you, where to get the information from and what to recommend buying. So ultimately, the AI bot guides your purchase decisions without you forming an affinity towards any other brand - or without even being aware of other brands. Big brands don't like that. </p><p>By providing SearchGPT as a separate service to ChatGPT, OpenAI is likely to give more options to publishers like Amazon or the NYTimes to form customer relationships and participate in their platform. NYT articles will link to their publisher, Amazon products will link to Amazon, and holiday prices will link to Expedia. </p><blockquote>SEO guess: From a technical SEO point of view, we should expect to see new bot definitions for SearchGPT, that allows publishers to share their content with SearchGPT but not ChatGPT.</blockquote>

OpenAI is testing a fully fledged Search Engine powered by ChatGPT: SearchGPT https://chatgpt.com/search.

The new iteration of the AI powered search engine brings mind blowing new features like.. links to content, images, video, live content and rich graphic experiences. Wow. Sounds like Google 15 years ago – you might think.

Despite none of this being "new", this is a milestone for OpenAI. The new interface opens up major opportunities and risks.


Is it mainstream yet?

Seems like it's takin

Where did you hear about us?

Aris Vrakas

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