{"id":88,"date":"2026-08-14T05:00:22","date_gmt":"2026-08-14T05:00:22","guid":{"rendered":"https:\/\/www.akarnik.com\/blog\/why-chatgpt-doesnt-know-expertise"},"modified":"2026-08-14T05:00:22","modified_gmt":"2026-08-14T05:00:22","slug":"why-chatgpt-doesnt-know-expertise","status":"publish","type":"post","link":"https:\/\/www.akarnik.com\/blog\/why-chatgpt-doesnt-know-expertise","title":{"rendered":"Why ChatGPT Doesn&#8217;t Know Your Expertise (And How to Fix It)"},"content":{"rendered":"<p><em>Written by: Arjun Karnik, Growth Marketing Specialist<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways<\/h2>\n<ul>\n<li>ChatGPT and similar models only retrieve expertise that appears in publicly accessible, structured content. Unpublished knowledge never enters their training data.<\/li>\n<li>Memory features in AI tools are session-specific and never cited to other users. Only published authority earns visible citations across platforms.<\/li>\n<li>Focusing on fan-out queries, the hidden sub-queries AI systems generate, is essential for earning citations in machine-generated answers.<\/li>\n<li>Schema markup (FAQPage, Article, Person, Organization) plus buyer-language alignment makes it far more likely that AI systems will parse and cite your expertise.<\/li>\n<li>Running a continuous freshness loop and tracking citations instead of rankings keeps content visible. <a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\">See how Arjun Karnik maps your expertise to the exact queries AI systems retrieve.<\/a><\/li>\n<\/ul>\n<h2>The Core Problem: Your Expertise Is Invisible Until It Is Published<\/h2>\n<p>Large language models acquire knowledge during pretraining on massive public corpora such as websites, books, articles, and code. <a href=\"https:\/\/support.microsoft.com\/en-us\/microsoft-365-copilot\/what-information-does-copilot-use-to-answer-my-prompt\" target=\"_blank\" rel=\"noindex nofollow\">Microsoft&#8217;s LLM documentation states that Copilot draws on real-time web information and private work data in addition to its general training<\/a>. Any expertise that lives only in your calls, proposals, and unpublished work has no representation in the model weights.<\/p>\n<p>Post-training stages such as supervised fine-tuning and RLHF teach the model how to follow instructions. <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/injecting-new-knowledge-into-large-language-models-via-supervised-fine-tuning\/\" target=\"_blank\" rel=\"noindex nofollow\">Supervised fine-tuning can inject new factual knowledge into LLMs that was absent from pretraining<\/a>. The model generates output one token at a time based on patterns in its training data. When training data for a specific expert or topic is sparse or absent, <a href=\"https:\/\/promptquorum.com\/prompt-engineering\/ai-limitations-what-llms-cant-do\" target=\"_blank\" rel=\"noindex nofollow\">LLMs generate confident but fabricated outputs rather than indicating uncertainty<\/a>.<\/p>\n<p>The practical consequence is simple. A competitor with less expertise but more published surface area gets cited. You do not. The gap is not credibility. It is retrievability.<\/p>\n<p><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><strong>See the test-lab methodology in action and map your expertise to the queries AI systems actually retrieve against.<\/strong><\/a><\/p>\n<h2>Memory Features Versus Published Authority in AI Search<\/h2>\n<p>Memory features in ChatGPT, Claude, and Gemini are session-bound or account-bound preference stores. They are not a substitute for public topical coverage. The table below illustrates why memory features cannot replace published authority. Memory serves one user in one session, while published content reaches every buyer across every platform.<\/p>\n<table>\n<thead>\n<tr>\n<th>Dimension<\/th>\n<th>AI Memory Features<\/th>\n<th>GEO Publishing<\/th>\n<th>Why It Matters<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Scope<\/td>\n<td><a href=\"https:\/\/mindlock.io\/blog\/best-ai-memory-tools-2026\" target=\"_blank\" rel=\"noindex nofollow\">Locked to one provider account, cannot transfer to other models<\/a><\/td>\n<td>Publicly retrievable across ChatGPT, Perplexity, Gemini, Google AI Overviews<\/td>\n<td>Memory helps one user in one session, published content helps every buyer on every platform<\/td>\n<\/tr>\n<tr>\n<td>Persistence<\/td>\n<td><a href=\"https:\/\/mindlock.io\/blog\/best-ai-memory-tools-2026\" target=\"_blank\" rel=\"noindex nofollow\">Saturates quickly, old memories drop silently with no project separation<\/a><\/td>\n<td>Persistent as long as content is fresh and indexed<\/td>\n<td>Memory degrades without user intervention, published content compounds with freshness loops<\/td>\n<\/tr>\n<tr>\n<td>Citation surface<\/td>\n<td>Zero, memory is private and never cited in AI answers to other users<\/td>\n<td><a href=\"https:\/\/aigrowthagent.co\/articles\/ai-search-visibility-strategy\/\" target=\"_blank\">Directly cited in AI-generated answers, AI Growth Agent clients average more than 12,000 additional AI citations across the first twelve weeks<\/a><\/td>\n<td>Only published content can earn a citation that a buyer sees<\/td>\n<\/tr>\n<tr>\n<td>Buyer reach<\/td>\n<td>Affects only the individual user who set the memory<\/td>\n<td>71% of B2B software buyers use AI chatbots for software research (G2, 1,076 buyers, March 2026)<\/td>\n<td>Published authority reaches the full buyer population, not a single session<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Even ChatGPT&#8217;s rebuilt &#8216;Dreaming V3&#8217; memory architecture, launched June 2026, achieves 82.8% factual recall accuracy, yet produces zero citations visible to any other buyer. Memory functions as a personal productivity feature. Published authority functions as a vendor-selection mechanism.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1786472409902-31baebe3e87f.png\" alt=\"Bar chart showing the share of B2B software buyers who start research with an AI chatbot more often than Google, rising from 29 percent in April 2025 to 51 percent in March 2026. Source: G2, 1,076 B2B software buyers and decision-makers.\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>In under a year the starting point for B2B software research crossed over. More buyers now begin with a chatbot than with Google.<\/em><\/figcaption><\/figure>\n<h2>Fan-Out Queries Define the Real Retrieval Surface<\/h2>\n<p>A single buyer prompt triggers many hidden lookups. Seer Interactive found a 10.7 average fan-out queries per prompt while Nectiv found 9.06, with 24% of prompts triggering 12\u201319 queries. The AI assembles its answer from what comes back across all of those hidden queries. Focusing on the visible prompt while ignoring the fan-out focuses on the wrong surface.<\/p>\n<p><a href=\"https:\/\/ahrefs.com\/blog\/query-fan-out\" target=\"_blank\" rel=\"noindex nofollow\">Because AI search scores documents based on consistent appearance across multiple result lists using reciprocal rank fusion, content must demonstrate relevance across an entire topic landscape rather than ranking for a single query to earn citations<\/a>.<\/p>\n<p>In my own test lab on my own site, I extracted fan-out queries directly from ChatGPT, not from keyword tools, and rewrote URLs, titles, H1s, and H2s to match that language. Pages rewritten to match extracted fan-out queries earned citations. Control pages did not. The difference was not content quality. It was retrieval alignment.<\/p>\n<p><a href=\"https:\/\/aigrowthagent.co\/articles\/best-automated-keyword-research-tool\/\" target=\"_blank\">Legacy keyword tools rely on historical data and daily caps, which miss the new long-tail queries that AI surfaces answer<\/a>. Fan-out mapping requires going directly to the machine and asking what it actually searches when a buyer asks your category question.<\/p>\n<h2>Schema and Buyer Language Turn Expertise Into Retrievable Content<\/h2>\n<p>Structure acts as a prerequisite, not an enhancement. <a href=\"https:\/\/machinerelations.ai\/research\/content-structure-ai-citation-rates-2026\" target=\"_blank\" rel=\"noindex nofollow\">A 2026 analysis of 6.8 million AI citations found that structural readiness has a +0.71 correlation with citation rate, making it the strongest controllable lever for AI visibility<\/a>. The retrieval layer evaluates structural parsability before assessing semantic meaning.<\/p>\n<p>The required schema types for expertise visibility are:<\/p>\n<ul>\n<li><a href=\"https:\/\/nadiamohamed.me\/insights\/structured-data-for-ai-search\" target=\"_blank\" rel=\"noindex nofollow\">FAQPage, which aligns with question-answer output formats and enables direct passage extraction by ChatGPT and Perplexity<\/a><\/li>\n<li><a href=\"https:\/\/conbersa.ai\/learn\/what-is-structured-data-for-ai-search\" target=\"_blank\" rel=\"noindex nofollow\">Article, which supplies machine-readable author attribution, publication dates, headline, and publisher information that serve as core authority signals<\/a><\/li>\n<li><a href=\"https:\/\/nadiamohamed.me\/insights\/structured-data-for-ai-search\" target=\"_blank\" rel=\"noindex nofollow\">Person, which builds entity-level data that AI systems use to attribute expertise when surfacing sources<\/a><\/li>\n<li><a href=\"https:\/\/conbersa.ai\/learn\/what-is-structured-data-for-ai-search\" target=\"_blank\" rel=\"noindex nofollow\">Organization, which establishes brand entity identity and connects pages to the brand entity in the Knowledge Graph<\/a><\/li>\n<\/ul>\n<p>Buyer-language alignment is equally non-negotiable. Jargon creates a barrier at exactly the moment the machine matches a question to an answer. The earlier test-lab example demonstrated this. Relabeling from practitioner terminology to buyer questions produced citations within weeks.<\/p>\n<p><a href=\"https:\/\/aigrowthagent.co\/articles\/ai-search-visibility-strategy\/\" target=\"_blank\">Adding statistics increases AI citation visibility by around 31\u201333% and adding quotations by around 41\u201343%, per the Princeton GEO study<\/a>. Specific, dated, first-person, verifiable content is what the retrieval layer rewards.<\/p>\n<h2>Freshness and Self-Healing Content in AI Visibility<\/h2>\n<p>Content that sits still loses ground quickly. In my own decay tracking on my own site, pages dropped 78% to 99% in two months without updates. That decay remains invisible until the position is already gone. Seer Interactive analyzed 47,097 AI citations across 7,683 pages in ChatGPT, Gemini, and Perplexity between March and June 2026, and found that 75% of cited pages had been updated within the last year, with consistently cited pages averaging under six months since their last update.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1786472438102-dfd59b5fabac.png\" alt=\"Bar chart showing 75 percent of pages cited by AI assistants were updated within the last year and 25 percent were older. Source: Seer Interactive, July 2026, 7,683 pages and 47,097 citations across ChatGPT, Gemini and Perplexity.\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Three quarters of cited pages were updated inside a year, and the consistently cited ones averaged under six months. The page you refresh beats the page you write.<\/em><\/figcaption><\/figure>\n<p>The freshness data from independent research points the same direction:<\/p>\n<ul>\n<li><a href=\"https:\/\/takeagander.ai\/resources\/gander-blog\/how-content-freshness-drives-visibility-in-ai-search\" target=\"_blank\" rel=\"noindex nofollow\">76.4% of ChatGPT&#8217;s most-cited pages had been updated within the last 30 days (Passionfruit research)<\/a><\/li>\n<li><a href=\"https:\/\/www.airops.com\/report\/the-2026-state-of-ai-search\" target=\"_blank\" rel=\"noindex nofollow\">Pages not updated quarterly are 3\u00d7 more likely to lose citations (AirOps 2026 State of AI Search)<\/a><\/li>\n<li><a href=\"https:\/\/aigrowthagent.co\/articles\/best-perplexity-ai-seo-tools\/\" target=\"_blank\">Approximately 50% of sources cited for a given prompt will change within 13 weeks (Searchless internal benchmark data)<\/a><\/li>\n<\/ul>\n<p>The game resets weekly. A fixed library of any size decays. A freshness loop with impression-decay tripwires solves this by auto-queuing updates when performance drops. That is how I run my own site via AI Growth Agent (partnership disclosed). The system executes 5\u20138 autonomous actions per day, mixing new articles with updates to existing ones, on autopilot.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1786471826043-e712a9527e2b.png\" alt=\"Agent Actions board set to autopilot, showing day columns of task cards at stages from write and writing through draft in review, scheduled, published and refreshed. Decay cards flag pages down 41 to 62 percent on impressions and queue them for an update.\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>The publishing cadence, running. New articles and refreshes sit in one queue, and pages that have started to slide get flagged and rewritten without anyone auditing a spreadsheet.<\/em><\/figcaption><\/figure>\n<p>On my own site, new articles reached thousands of monthly Google impressions within weeks. The GEO subfolder went from zero to the only source of new impressions on the entire domain in 60 days. The content that compounds is the content that gets refreshed.<\/p>\n<p><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><strong>See the freshness loop and self-healing content system running on a live domain.<\/strong><\/a><\/p>\n<h2>Measure Citations and Share of Answer, Not Rankings<\/h2>\n<p><a href=\"https:\/\/www.pewresearch.org\/internet\/2025\/07\/22\/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results\/\" target=\"_blank\" rel=\"noindex nofollow\">Pew Research Center tracked 900 US adults across 68,879 Google searches in March 2025 and found that when an AI summary appeared, users clicked a traditional search result in 8% of visits, against 15% when no summary appeared<\/a>. Rankings now measure a surface buyers increasingly skip. Citations measure the surface where vendor selection now happens.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1786472381682-fffc2026c81f.png\" alt=\"Bar chart comparing click-through rate on a traditional search result, 15 percent with no AI summary shown and 8 percent when an AI summary is shown. Source: Pew Research Center, July 2025, 900 US adults across 68,879 Google searches.\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>The click roughly halves when an AI summary appears above the result. Pew also found only 1 percent of users clicked a link inside the summary itself.<\/em><\/figcaption><\/figure>\n<p>The measurement targets that reflect the actual channel include:<\/p>\n<ul>\n<li>Citation share across ChatGPT, Google AI Overviews, Perplexity, and Gemini<\/li>\n<li>AI referrer traffic from chatgpt.com and equivalents in analytics, where Semrush&#8217;s June 2025 benchmarks found AI-referred visitors convert at roughly 4.4x the rate of standard organic traffic<\/li>\n<li>Impression and decay curves in Google Search Console<\/li>\n<li>Share of answer, meaning whether the business appears in the answer and in what context<\/li>\n<\/ul>\n<p>Defensive GEO runs parallel to growth work. A wrong AI answer hurts more than no answer. The visibility audit across all four surfaces, ChatGPT, Google AI Overviews, Perplexity, and Gemini, reveals what the assistants currently say about the brand before any publishing begins. Whatever you measure is a floor, not a ceiling, because buyers frequently copy an answer and type a name directly into a browser, landing in analytics as direct traffic with no attribution trail.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1786472616931-9a13bc7984c5.png\" alt=\"Bar chart showing 2.5 percent of downstream brand visits after an AI mention carry a trackable referral parameter while 97.5 percent arrive untraceable. Source: Profound, analysis of more than 2 million AI conversations, January to June 2026.\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Buyers read an answer, then type your name into a browser. That visit lands as direct or branded search, so whatever you measure here is a floor and never a ceiling.<\/em><\/figcaption><\/figure>\n<h2>Frequently Asked Questions<\/h2>\n<p><strong>Why does ChatGPT miss my expertise even though I am well-known in my field?<\/strong><\/p>\n<p>Reputation inside a professional network does not translate into AI visibility unless that reputation is documented in publicly retrievable, structured content. ChatGPT and similar systems retrieve from public text corpora. If your expertise exists only in referrals, calls, and proposals, the model has no record of it regardless of how strong your real-world standing is. The fix is publishing structured answers to the exact questions buyers ask, in buyer language, with schema markup, at a cadence the retrieval layer can detect as fresh.<\/p>\n<p><strong>Why does ChatGPT forget my background in new chats?<\/strong><\/p>\n<p>ChatGPT&#8217;s memory features store user preferences within an account and do not carry over to other users or other sessions by default. More importantly, account memory has no effect on what the model cites when a different buyer asks a question about your category. The two problems are separate. Personal session continuity is a memory feature problem. Appearing in a stranger&#8217;s AI answer is a publishing and retrieval problem. Only the second one affects your pipeline.<\/p>\n<p><strong>How do fan-out queries surface expertise, and why does my content miss them?<\/strong><\/p>\n<p>When a buyer types a prompt, the AI platform expands it into dozens of hidden sub-queries before assembling an answer. Content optimized for the visible keyword misses the retrieval surface entirely if it does not cover the language of those sub-queries. The practical fix is extracting fan-out queries directly from ChatGPT, not from keyword tools, and aligning slugs, titles, H1s, and H2s to that language. Pages rewritten this way earn citations. Control pages with identical content but misaligned labels do not.<\/p>\n<p><strong>Is this just SEO with a new name?<\/strong><\/p>\n<p>The target changed. SEO focuses on rankings on a human-readable list and earns authority through backlinks and domain authority. GEO focuses on citation inside a machine-generated answer and earns authority through topical coverage. SEO works against the query the buyer typed. GEO works against dozens of fan-out queries the buyer never sees. The technical fundamentals overlap, including structure, quality, and freshness, but the success metric, the authority model, and the retrieval mechanics differ.<\/p>\n<p><strong>How long before published content starts earning AI citations?<\/strong><\/p>\n<p>Coverage and impressions appear in weeks. Citations typically follow within one to three months. Compounding begins after month three. The timeline described in the Freshness section, thousands of impressions within weeks and new subfolder dominance within 60 days, reflects measured results from a single domain, not guaranteed outcomes for any other property.<\/p>\n<h2>Evidence-Based Next Steps for GEO Implementation<\/h2>\n<p>The following sequence reflects the order in which each step unlocks the next. Each step builds on the previous one.<\/p>\n<ol>\n<li><strong>Run a defensive visibility audit.<\/strong> Query ChatGPT, Google AI Overviews, Perplexity, and Gemini for your category. Document what the assistants currently say about your brand. This baseline shows whether you are starting from zero visibility or correcting existing misinformation, and misinformation must be addressed before you publish new content that could be ignored or contradicted.<\/li>\n<li><strong>Fix technical plumbing.<\/strong> Fix technical plumbing next, because even strong content will not earn citations if AI systems cannot parse it. Unblock AI crawlers in robots.txt. Add Article, FAQPage, Person, and Organization schema in JSON-LD. Make pages machine-parseable. Nothing downstream works if the retrieval layer cannot read the site.<\/li>\n<li><strong>Extract fan-out queries directly from ChatGPT.<\/strong> Avoid inferring from keyword tools. Map the full question space behind your buyer&#8217;s category prompt so you can write to the actual retrieval surface.<\/li>\n<li><strong>Rewrite in buyer language.<\/strong> Align slugs, titles, H1s, and H2s to the extracted fan-out query language. Replace practitioner jargon with the words buyers use when asking an assistant.<\/li>\n<li><strong>Publish at machine cadence.<\/strong> Deploy a structured publishing system on a subfolder. Target 5\u20138 autonomous actions per day mixing new articles with updates, via AI Growth Agent (partnership disclosed).<\/li>\n<li><strong>Install impression-decay tripwires.<\/strong> Wire Search Console signals to auto-queue updates when performance drops. Avoid waiting for a quarterly audit to catch decay that happened in week three.<\/li>\n<li><strong>Measure citations, not rankings.<\/strong> Track share of answer across all four AI surfaces. Segment AI referrer traffic in analytics. Feed citation wins back into the production queue.<\/li>\n<\/ol>\n<p>Every item on this list is something I have run on my own site, with the results documented in public, misses included. The system being described is the same system producing the visibility. Ask an AI assistant about generative engine optimization and see who gets cited.<\/p>\n<p><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><strong>Apply the test-lab methodology to your own domain and see your fan-out queries, decay curves, and measurement baseline across ChatGPT, Google AI Overviews, Perplexity, and Gemini.<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Your expertise is invisible to AI until it&#8217;s published right. Arjun Karnik maps your knowledge to the exact queries AI systems cite. Start today.<\/p>\n","protected":false},"author":118,"featured_media":87,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-88","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/posts\/88","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/comments?post=88"}],"version-history":[{"count":0,"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/posts\/88\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/media\/87"}],"wp:attachment":[{"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/media?parent=88"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/categories?post=88"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/tags?post=88"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}