--- title: "GEO Strategy for Google AI Overviews: 8-Step Playbook (2026)" url: https://alicelabs.ai/en/insights/geo-strategy-ai-overviews description: "Build a research-backed GEO strategy for Google AI Overviews. Apply Aggarwal's top 3 tactics, deploy schema, set up llms.txt, and track LLM citations quarterly." lang: en --- Alice Labs (https://alicelabs.ai/en/) Services What we do (https://alicelabs.ai/#welcome)About Alice (https://alicelabs.ai/#who-we-are)Case (https://alicelabs.ai/en/case)Insights (https://alicelabs.ai/en/insights)Contact (https://alicelabs.ai/#email-form) AI Search & LLMO How-to Guide Fresh Last reviewed: 14 September 2026 · 30d ago # GEO Strategy for Google AI Overviews: An 8-Step Playbook ## TL;DR Quick Answer Cited by AI > A GEO strategy for Google AI Overviews applies Aggarwal et al.'s validated tactics (citations, statistics, quotation) on top of strong E-E-A-T, Schema.org markup, and an llms.txt file. Audit current AI Overview presence, optimize content with citation-rich evidence, deploy structured data, and track LLM citations quarterly. Google AI Overviews launched broadly in May 2024 and now sit above the blue links for an expanding share of queries. This playbook turns the academic GEO research (Aggarwal et al., 2024) into an operational program for AI Overview visibility. A GEO strategy (Generative Engine Optimization strategy) is a structured program for making your content cited inside AI-generated answers. The term was coined by Aggarwal et al. in 2024 (arXiv:2311.09735, Princeton, Georgia Tech, IIT Delhi, Allen Institute for AI). For Google AI Overviews specifically, GEO combines Aggarwal's validated tactics (citations, statistics, quotation) with E-E-A-T signals, Schema.org markup, and entity authority — the inputs Gemini's grounding layer relies on. Time 8-12 weeks initial sprint, ongoing Difficulty Intermediate Tools Google Search Console (impressions, position, AI Overview hints), Google Rich Results Test (schema validation), Schema.org JSON-LD generator… ## Before you start - Functional SEO foundation (crawlable site, indexable content, basic schema) - Author identities with credentials and LinkedIn profiles - CMS access for schema and content updates - Familiarity with E-E-A-T and Schema.org basics ## What you'll have at the end A GEO program that earns AI Overview citations on your priority prompts, defended by E-E-A-T signals and structured data, with quarterly tracking against a peer benchmark. Image: Linus Ingemarsson - Author at Alice Labs (https://alicelabs.ai/images/linus-ingemarsson.png) Written by Linus Ingemarsson: https://www.linkedin.com/in/linus-ingemarsson/ Image: Eric Lundberg - Reviewer at Alice Labs (https://alicelabs.ai/images/eric-lundberg.png) Reviewed by Eric Lundberg: https://www.linkedin.com/in/eric-lundberg-3530451bb/ Published May 6, 2026 · Updated September 14, 2026 12 min read ## 8-step process 0/8 complete 1. 01 #### Step 1: Audit your current AI Overview presence Define 30-50 high-intent prompts your audience asks Google. Run each query and log whether an AI Overview triggers, which sources are cited, and where you appear (or don't). Capture screenshots, citation positions, and competitor URLs. This baseline is your reference for every later iteration. 2. 02 #### Step 2: Apply Aggarwal's top 3 GEO tactics: citations, statistics, quotation From the GEO paper (Aggarwal et al., 2024, arXiv:2311.09735), three of nine tested methods produced the strongest visibility lift. Add inline citations to authoritative sources, include specific statistics with named providers, and use direct quotations from recognized experts. These three together delivered up to ~40% visibility uplift in the paper's benchmarks. 3. 03 #### Step 3: Strengthen E-E-A-T across every key page Google's E-E-A-T framework (Experience, Expertise, Authoritativeness, Trustworthiness) is the quality signal AI Overviews lean on. Add author bylines with credentials, reviewer signatures, last-reviewed dates, and clear About/Contact pages. Link every author to a real LinkedIn profile and consistent biography across the web. 4. 04 #### Step 4: Deploy Schema.org markup AI Overviews can consume AI Overviews ingest structured data to ground answers. Implement JSON-LD for Article, FAQPage, HowTo, Organization, and Person on every relevant page. Validate with Google's Rich Results Test before deploying. Malformed schema is worse than none — it can break extraction. 5. 05 #### Step 5: Set up an llms.txt file at your domain root The llms.txt standard (Answer.AI, Jeremy Howard, September 2024) provides a machine-readable site summary for AI crawlers. Place it at yourdomain.com/llms.txt. Include site purpose, key pages, product summaries, and documentation links in markdown format. It costs nothing and signals AI-readiness. 6. 06 #### Step 6: Build entity authority signals across the web AI Overviews trust sources that are recognized as entities. Earn brand mentions in industry publications, ensure consistent NAP and About-page entity descriptions, claim Wikidata where appropriate, and align Schema.org Organization markup with external profiles. Entity recognition compounds quarter over quarter. 7. 07 #### Step 7: Monitor with the Alice Labs LLMO Citation Benchmark Track LLM citation share across ChatGPT, Perplexity, Claude, and Google AI Overviews quarterly. The Alice Labs LLMO Citation Benchmark (100 SaaS brands, quarterly) provides peer-comparison context. Combine it with manual prompt audits and GA4 referral data from chatgpt.com, perplexity.ai, and Gemini-driven sources. 8. 08 #### Step 8: Iterate quarterly on underperforming prompts Every 90 days, revisit the prompts where you lost citation share or never appeared. Apply the GEO tactics again — sharpen citations, refresh statistics, upgrade schema, expand FAQ. One Alice Labs media-company client achieved a +2,092% click increase via this iteration loop applied to GEO-optimized content. ## Key Takeaways - GEO was coined in Aggarwal et al. 2024 (arXiv:2311.09735) and tested 9 candidate methods on a benchmark of generative-engine queries. - The top 3 winning tactics were citations, statistics, and quotation — together producing up to ~40% visibility lift in the paper's benchmarks. - Google AI Overviews launched broadly in May 2024, replaced the SGE label, and are powered by Gemini using organic ranking + grounding. - Schema.org structured data is consumed directly by AI Overviews — entity, Article, FAQPage, and HowTo schema all increase grounding confidence. - E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is Google's quality framework and the foundation AI Overviews lean on. - Roughly 58.5% of US searches and 59.7% of EU searches ended without a click in 2024 (SparkToro / Datos) — citation share is the new visibility metric. 01 / 05 Step ## What GEO Actually Is (Aggarwal's Definition) In short GEO (Generative Engine Optimization) was coined in Aggarwal et al. 2024 (arXiv:2311.09735) by researchers from Princeton, Georgia Tech, IIT Delhi, and the Allen Institute for AI. It is defined as the practice of optimizing content to be cited inside AI-generated answers from generative engines. The paper formalized GEO as a research problem and built GEO-bench — a benchmark of real generative-engine queries used to test optimization strategies. Before this paper, "generative engine optimization" did not exist as a defined term in the academic literature. The authors framed GEO as distinct from traditional SEO. SEO targets ranking position on a SERP. GEO targets being cited as a source inside an AI-generated answer. For Google AI Overviews, this distinction matters operationally. AI Overviews sit above the blue links and synthesize answers from multiple sources. Being cited inside that synthesis is now a separate visibility outcome from ranking #1 organically. For the delivery playbook we use, see our ai seo services (https://alicelabs.ai/en/ai-seo). 02 / 05 Step ## The 9 Methods Tested + Why Citations, Statistics, Quotation Won In short Aggarwal et al. tested 9 candidate GEO methods on their benchmark. Three produced the strongest visibility lift: citations, statistics, and quotation — together yielding up to ~40% improvement. The other six (authoritative tone, fluency, simplification, technical jargon, easy-to-understand, unique words) had smaller or neutral effects. The 9 methods tested in the paper were: - **Authoritative tone** — rewrite content in a confident, expert voice. - **Citations** — add inline references to named sources. - **Statistics** — insert specific quantitative data with attribution. - **Quotation** — include direct quotes from recognized experts. - **Fluency** — improve sentence flow without adding new evidence. - **Simplification** — reduce reading level. - **Technical jargon** — increase domain-specific vocabulary. - **Easy-to-understand** — rewrite for accessibility. - **Unique words** — vary vocabulary diversity. The three winners — citations, statistics, quotation — share a common mechanism. Each one adds _verifiable, attributable evidence_ the generative engine can extract and re-present with confidence. The losing methods either changed surface form without adding evidence (fluency, unique words) or worked against extractability (excess jargon). The lesson: generative engines reward new substance, not polish. Image: Linus Ingemarsson (https://alicelabs.ai/images/linus-ingemarsson.png) Image: Eric Lundberg (https://alicelabs.ai/images/eric-lundberg.png) Alice Labs practitioner team ## Talk to the team behind 100+ AI implementations 30-minute discovery call with a senior Alice Labs consultant. No slide deck, no sales pitch — just a scoping conversation. Book a Discovery Call 03 / 05 Step ## GEO vs SEO: Overlap and Divergence In short GEO and SEO share the same foundation — a crawlable site, structured data, E-E-A-T, authoritative content. They diverge on three axes: format (extractable answers vs long-form), signal weight (entity recognition vs backlinks), and measurement (per-prompt citation share vs per-query rankings). The overlap is large. Both disciplines reward clean technical foundations, Schema.org markup, fresh dated content, and inbound trust signals. A site that fails technical SEO will fail at GEO too. The divergence is tactical, not philosophical: 1. **Format.** SEO tolerates long-form content. GEO rewards extractable answer blocks — direct definitions, FAQs, captioned data tables — that an LLM can lift as a self-contained snippet. 2. **Signal weight.** SEO weights backlinks heavily. GEO weights entity recognition (Wikipedia presence, Knowledge Graph, consistent brand mentions) more heavily than raw link volume. 3. **Measurement.** SEO is measured per-query in Search Console. GEO is measured per-prompt across multiple LLMs — newer tooling, no single console. The operating model that works: one content program, two dashboards. Run SEO and GEO with shared inputs (briefs, schema, entity clarity) and separate reporting cadences. For the software layer that supports that split, see our roundup of ai content optimization tools (https://alicelabs.ai/en/insights/ai-content-optimization-tools). Alice Labs practitioner team ## Want a GEO program built around verified research? We build GEO + SEO programs grounded in Aggarwal's validated tactics, deployed across 100+ Nordic enterprise implementations and tracked with the Alice Labs LLMO Citation Benchmark. Book a GEO strategy call 04 / 05 Step ## GEO for Google AI Overviews Specifically (Schema, E-E-A-T) In short Google AI Overviews launched broadly in May 2024 (rebranded from SGE) and are powered by Gemini using organic ranking plus a grounding layer. Schema.org structured data is consumed directly. E-E-A-T is the quality framework AI Overviews lean on for source selection. AI Overviews don't replace organic ranking — they sit on top of it. Gemini selects candidate sources from the organic ranking pool, then uses grounding signals (schema, entity recognition, freshness, E-E-A-T) to decide which sources to cite inside the synthesized answer. For AI Overview optimization, prioritize the schema types Google has documented as relevant: - **Article / NewsArticle.** Declares headline, author, datePublished, dateModified. Foundational for every page. - **FAQPage.** Self-contained Q&A blocks the grounding layer can lift verbatim into AI Overviews. - **HowTo.** Step-by-step procedures, frequently surfaced for procedural queries. - **Organization + Person.** Establishes who publishes, who authored, what credentials apply — the structured-data half of E-E-A-T. E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is Google's quality framework. For AI Overviews, it expresses through visible author bylines with credentials, reviewer signatures, citation density, and inbound mention quality. 05 / 05 Step ## Measurement: LLM Citation Tracking In short GEO measurement requires three layers: manual prompt audits across 30-50 priority prompts, automated AI-visibility tools, and the Alice Labs LLMO Citation Benchmark for peer-relative context. There is no single 'GEO Search Console' yet. The SparkToro / Datos 2024 zero-click study found ~58.5% of US searches and ~59.7% of EU searches ended without a click. In that environment, citation share inside AI Overviews is increasingly the visibility metric that matters. A measurement stack that works: 1. **Manual prompt audits.** Define 30-50 priority prompts. Run them weekly across Google AI Overviews, ChatGPT, Perplexity, and Claude. Log citation presence, position, and competitors. 2. **Alice Labs LLMO Citation Benchmark.** Quarterly, 100-SaaS-brand benchmark. Use it as peer-comparison context — are you gaining or losing citation share relative to the cohort? 3. **GA4 referral analytics.** Track chatgpt.com, perplexity.ai, and Gemini-related referrers. Filter to confirm click-through patterns. One Alice Labs media-company client achieved a +2,092% click increase after applying Aggarwal's top 3 tactics on top of strong technical SEO, iterated quarterly. The GEO loop compounds when measurement is real and iteration is disciplined. Alice Labs practitioner team Book a Discovery Call ## About the Authors & Reviewers Written by Image: Linus Ingemarsson - CEO & Co-Founder, Alice Labs at Alice Labs (https://alicelabs.ai/images/linus-ingemarsson.png) Linus Ingemarsson (https://www.linkedin.com/in/linus-ingemarsson/) CEO & Co-Founder, Alice Labs CEO & Co-Founder at Alice Labs. Author of 7 research reports on AI adoption, governance and labor markets cited across EU, OECD and US benchmarks. - 8+ years in AI strategy & implementation - Top-5 AI Speaker, Sweden (Mindley 2025) - 100+ enterprise AI engagements View profile: https://www.linkedin.com/in/linus-ingemarsson/ Linus Ingemarsson on LinkedIn: https://www.linkedin.com/in/linus-ingemarsson/ Email Linus Ingemarsson: linus@alicelabs.ai Reviewed by September 14, 2026 Image: Eric Lundberg - Co-Founder, Alice Labs at Alice Labs (https://alicelabs.ai/images/eric-lundberg.png) Eric Lundberg (https://www.linkedin.com/in/eric-lundberg-3530451bb/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Builds AI automation, agent workflows and integration systems that hold up in real business operations. - AI automation & agent systems lead - Workflow design across 100+ deployments - Specialist in RAG, integrations & APIs View profile: https://www.linkedin.com/in/eric-lundberg-3530451bb/ Eric Lundberg on LinkedIn: https://www.linkedin.com/in/eric-lundberg-3530451bb/ Email Eric Lundberg: eric@alicelabs.ai Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Frequently Asked Questions ### What is a GEO strategy? A GEO strategy is a structured program for getting your content cited inside AI-generated answers. The term GEO (Generative Engine Optimization) was coined in Aggarwal et al. 2024 (arXiv:2311.09735) and validated 9 candidate methods, with citations, statistics, and quotation producing the strongest visibility lift. ### When did Google AI Overviews launch? Google AI Overviews launched broadly in May 2024, rebranded from the earlier 'Search Generative Experience' (SGE) label. They are powered by Gemini and use the organic ranking pool plus a grounding layer to select cited sources. ### Which GEO tactics actually work according to research? Aggarwal et al. (2024) tested 9 GEO methods on a benchmark of generative-engine queries. Three won decisively: citations, statistics, and quotation — together producing up to ~40% visibility lift. The other six (fluency, simplification, technical jargon, easy-to-understand, unique words, authoritative tone alone) had smaller or neutral effects. ### Is GEO different from SEO for AI Overviews? GEO and SEO share the same foundation but optimize for different outcomes. SEO targets ranking in blue links. GEO targets citation inside AI-generated answers. For Google AI Overviews specifically, GEO sits on top of SEO — Gemini draws from the organic ranking pool, then applies grounding signals to choose which sources to cite. ### Does Schema.org markup help with AI Overviews? Yes. Schema.org structured data is consumed by Google AI Overviews as a grounding signal. Article, FAQPage, HowTo, Organization, and Person schema (in JSON-LD) all increase the confidence with which Gemini can extract and attribute claims from your pages. ### How important is E-E-A-T for GEO? E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is Google's quality framework and a foundational input AI Overviews lean on for source selection. Visible author bylines with credentials, reviewer signatures, last-reviewed dates, and authoritative inbound mentions all strengthen E-E-A-T signals. ### How do I measure GEO success for AI Overviews? Use a three-layer measurement stack: weekly manual prompt audits across 30-50 priority prompts, the Alice Labs LLMO Citation Benchmark for quarterly peer-relative context, and GA4 referral analytics. There is no single GEO Search Console — citation share across multiple LLMs is the new visibility metric. ### How long does a GEO strategy take to show results? Plan an 8-12 week initial sprint to apply Aggarwal's top 3 tactics, deploy schema, set up llms.txt, and build the measurement stack. Technical changes (schema, llms.txt) can influence citations within days. Entity-authority and E-E-A-T compounding typically takes 60-120 days, with quarterly iteration sustaining and growing gains. Previous in AI Search & LLMO ### GEO Audit Checklist: Is Your Site AI-Search Ready? (12 Categories) https://alicelabs.ai/en/insights/geo-audit-checklist Next in AI Search & LLMO ### AI Search ROI: Is Optimizing for ChatGPT & Perplexity Worth It? https://alicelabs.ai/en/insights/ai-search-roi ## Further reading - Aggarwal et al. — GEO: Generative Engine Optimization (arXiv:2311.09735) (https://arxiv.org/abs/2311.09735)· arxiv.org - llms.txt — Answer.AI proposal (Jeremy Howard, Sep 2024) (https://llmstxt.org/)· llmstxt.org - Schema.org — official structured data vocabulary (https://schema.org/)· schema.org ## Related reading comparison ### GEO vs SEO: What's the Difference? Comparison of generative engine optimization and search engine optimization, with the academic source. 8 min https://alicelabs.ai/en/insights/geo-vs-seo glossary ### What Is LLMO? Large Language Model Optimization Explained Glossary article defining the industry-side companion term to GEO. 7 min https://alicelabs.ai/en/insights/what-is-llmo explainer ### Google AI Overviews Explained How Gemini-powered AI Overviews work and which signals they weight. 9 min https://alicelabs.ai/en/insights/google-ai-overviews-explained ## Sources 1. Aggarwal et al. — GEO: Generative Engine Optimization (arXiv:2311.09735, 2024) (https://arxiv.org/abs/2311.09735)(accessed 2026-05-06) 2. Google — Generative AI in Search expanding (May 2024 announcement) (https://blog.google/products/search/generative-ai-google-search-may-2024/)(accessed 2026-05-06) 3. Jeremy Howard / Answer.AI — llms.txt proposal (September 2024) (https://llmstxt.org/)(accessed 2026-05-06) 4. Schema.org — official structured data vocabulary (https://schema.org/)(accessed 2026-05-06) 5. Google Search Central — Structured data general guidelines (https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data)(accessed 2026-05-06) 6. Google Search Central — Creating helpful, reliable, people-first content (E-E-A-T) (https://developers.google.com/search/docs/fundamentals/creating-helpful-content)(accessed 2026-05-06) 7. SparkToro / Datos — 2024 zero-click search analysis (https://sparktoro.com/blog/in-2024-we-finally-satisfyingly-measured-how-much-of-google-search-traffic-goes-to-google-properties/)(accessed 2026-05-06) Next scheduled review: 2026-12-13 Alice Labs practitioner team Book a Discovery Call Share Share on LinkedIn (https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Fgeo-strategy-ai-overviews)Share on X (https://twitter.com/intent/tweet?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Fgeo-strategy-ai-overviews&text=GEO%20Strategy%20for%20Google%20AI%20Overviews%3A%208-Step%20Playbook%20(2026)) ## Get in Touch! The lab usually responds within 24 hours. Need help with AI? 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The other six (fluency, simplification, technical jargon, easy-to-understand, unique words, authoritative tone alone) had smaller or neutral effects." } }, { "@type": "Question", "name": "Is GEO different from SEO for AI Overviews?", "acceptedAnswer": { "@type": "Answer", "text": "GEO and SEO share the same foundation but optimize for different outcomes. SEO targets ranking in blue links. GEO targets citation inside AI-generated answers. For Google AI Overviews specifically, GEO sits on top of SEO — Gemini draws from the organic ranking pool, then applies grounding signals to choose which sources to cite." } }, { "@type": "Question", "name": "Does Schema.org markup help with AI Overviews?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. Schema.org structured data is consumed by Google AI Overviews as a grounding signal. Article, FAQPage, HowTo, Organization, and Person schema (in JSON-LD) all increase the confidence with which Gemini can extract and attribute claims from your pages." } }, { "@type": "Question", "name": "How important is E-E-A-T for GEO?", "acceptedAnswer": { "@type": "Answer", "text": "E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is Google's quality framework and a foundational input AI Overviews lean on for source selection. Visible author bylines with credentials, reviewer signatures, last-reviewed dates, and authoritative inbound mentions all strengthen E-E-A-T signals." } }, { "@type": "Question", "name": "How do I measure GEO success for AI Overviews?", "acceptedAnswer": { "@type": "Answer", "text": "Use a three-layer measurement stack: weekly manual prompt audits across 30-50 priority prompts, the Alice Labs LLMO Citation Benchmark for quarterly peer-relative context, and GA4 referral analytics. There is no single GEO Search Console — citation share across multiple LLMs is the new visibility metric." } }, { "@type": "Question", "name": "How long does a GEO strategy take to show results?", "acceptedAnswer": { "@type": "Answer", "text": "Plan an 8-12 week initial sprint to apply Aggarwal's top 3 tactics, deploy schema, set up llms.txt, and build the measurement stack. Technical changes (schema, llms.txt) can influence citations within days. Entity-authority and E-E-A-T compounding typically takes 60-120 days, with quarterly iteration sustaining and growing gains." } } ] }, { "@context": "https://schema.org", "@type": "HowTo", "name": "GEO Strategy for Google AI Overviews: An 8-Step Playbook", "description": "Build a research-backed GEO strategy for Google AI Overviews. 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These three together delivered up to ~40% visibility uplift in the paper's benchmarks." }, { "@type": "HowToStep", "position": 3, "name": "Strengthen E-E-A-T across every key page", "text": "Google's E-E-A-T framework (Experience, Expertise, Authoritativeness, Trustworthiness) is the quality signal AI Overviews lean on. Add author bylines with credentials, reviewer signatures, last-reviewed dates, and clear About/Contact pages. Link every author to a real LinkedIn profile and consistent biography across the web." }, { "@type": "HowToStep", "position": 4, "name": "Deploy Schema.org markup AI Overviews can consume", "text": "AI Overviews ingest structured data to ground answers. Implement JSON-LD for Article, FAQPage, HowTo, Organization, and Person on every relevant page. Validate with Google's Rich Results Test before deploying. Malformed schema is worse than none — it can break extraction." }, { "@type": "HowToStep", "position": 5, "name": "Set up an llms.txt file at your domain root", "text": "The llms.txt standard (Answer.AI, Jeremy Howard, September 2024) provides a machine-readable site summary for AI crawlers. Place it at yourdomain.com/llms.txt. Include site purpose, key pages, product summaries, and documentation links in markdown format. It costs nothing and signals AI-readiness." }, { "@type": "HowToStep", "position": 6, "name": "Build entity authority signals across the web", "text": "AI Overviews trust sources that are recognized as entities. Earn brand mentions in industry publications, ensure consistent NAP and About-page entity descriptions, claim Wikidata where appropriate, and align Schema.org Organization markup with external profiles. Entity recognition compounds quarter over quarter." }, { "@type": "HowToStep", "position": 7, "name": "Monitor with the Alice Labs LLMO Citation Benchmark", "text": "Track LLM citation share across ChatGPT, Perplexity, Claude, and Google AI Overviews quarterly. The Alice Labs LLMO Citation Benchmark (100 SaaS brands, quarterly) provides peer-comparison context. Combine it with manual prompt audits and GA4 referral data from chatgpt.com, perplexity.ai, and Gemini-driven sources." }, { "@type": "HowToStep", "position": 8, "name": "Iterate quarterly on underperforming prompts", "text": "Every 90 days, revisit the prompts where you lost citation share or never appeared. Apply the GEO tactics again — sharpen citations, refresh statistics, upgrade schema, expand FAQ. 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