How to Rank in AI Overviews and LLMs: The Complete GEO Guide (2026)

AI Overviews, ChatGPT, and Perplexity AI have fundamentally changed how people discover information online. Instead of clicking through ten blue links, users now receive a single synthesised answer — generated by a large language model (LLM) — that draws from multiple sources across the web. The source that gets cited is the source that wins the traffic. This guide explains exactly how to become that source.

The discipline of optimising content for AI-generated search results is called Generative Engine Optimisation (GEO). It is not a replacement for traditional SEO — it builds on top of it. But it requires a meaningfully different set of tactics, because the systems selecting and citing your content are not keyword-matching algorithms. They are language models that evaluate meaning, authority, structure, and factual clarity.

What are AI Overviews and how do they work?

Google AI Overviews (formerly Search Generative Experience, or SGE) are AI-generated response boxes that appear at the top of Google search results for eligible queries. They synthesise information from multiple web pages and present a structured, conversational answer — with citations linking back to the source pages.

Google's AI Overviews are powered by Gemini, Google's multimodal large language model. The system performs a retrieval step (finding relevant pages from Google's existing index), a generation step (drafting the overview using retrieved content), and a citation step (linking back to the sources it drew from). This means only indexed, ranking pages are eligible — AI Overviews are not a separate index.

AI Overviews currently appear most frequently for informational queries — how-to guides, definitions, comparisons, and explanations. They appear less frequently for transactional queries (where Google still defaults to shopping and local results) and navigational queries (where users are looking for a specific brand or website).

GEO vs traditional SEO: what's different?

Traditional SEO optimises for ranking signals that help algorithms surface your page in a ranked list. Click-through rate, dwell time, and position are the primary success metrics. GEO optimises for a fundamentally different outcome: being synthesised and cited within a generated answer, where your page may receive traffic without ever being seen as a ranked result.

Crucially, traditional SEO and GEO are not competing strategies — they are complementary. Strong organic rankings remain the gateway to AI Overview citations. GEO tactics layer on top of, and reinforce, traditional SEO best practices.

How LLMs decide which sources to cite

Understanding the selection mechanism is the key to optimising for it. While each platform has proprietary systems, the general process follows a consistent pattern across all major AI search tools:

The practical implication: your content must be retrievable (good SEO), trustworthy (strong E-E-A-T), and extractable (clear structure and direct answers). Failing on any one of these dimensions reduces citation likelihood significantly.

Platform differences: Google AIO vs ChatGPT vs Perplexity

Each major AI search platform has distinct characteristics that should inform your optimisation approach. A single piece of content can perform well across all of them, but understanding the nuances helps you prioritise.

E-E-A-T: the trust foundation for AI citations

E-E-A-T — Experience, Expertise, Authoritativeness, and Trustworthiness — was introduced by Google as a quality framework for its human search quality raters. It has since become a core signal in how Google's AI systems evaluate source reliability. Pages that demonstrate strong E-E-A-T are significantly more likely to be cited in AI Overviews.

Each dimension of E-E-A-T can be actively strengthened:

Experience

Demonstrate first-hand, real-world experience with the topic. This means incorporating original examples, case studies, screenshots, test results, and personal observations that could only come from someone who has actually done the thing being discussed. AI systems — and Google's quality raters — can recognise the difference between content written from experience and content that regurgitates what other sources say.

Expertise

Author pages with detailed credentials, professional history, and relevant qualifications significantly strengthen expertise signals. Link author profiles to external verification — LinkedIn profiles, academic publications, professional body memberships, or named contributions to industry publications. For YMYL (Your Money or Your Life) topics like finance, health, and legal content, demonstrated expertise is non-negotiable for AI citation.

Authoritativeness

Authority is primarily established through inbound links from other authoritative sources in your field. Earning mentions, citations, and links from recognised industry publications, academic institutions, and established media outlets signals to AI systems that your content is considered a reliable reference by the wider web. Original research, proprietary data, and unique studies that other sites cite are the most powerful authority builders available.

Trustworthiness

Trust signals include: HTTPS security, transparent ownership (About page, company information, contact details), named authors with verifiable credentials, citations of primary sources, and a track record of factual accuracy. Pages without any identifiable author or publisher are treated with significant scepticism by both Google's quality systems and LLM citation algorithms.

How to structure content for AI readability

The way you organise information on a page is one of the highest-leverage GEO optimisations available. LLMs parse content by breaking it into chunks and evaluating each chunk's relevance and clarity. Poorly structured content — where the answer to a question is buried in the middle of a 300-word paragraph — is consistently overlooked in favour of content that labels and structures its answers explicitly.

The following structural principles maximise AI extractability:

• Use question-format headings (H2/H3). Heading text that mirrors the user's natural query language makes it trivially easy for AI systems to match the heading to the query intent, then extract the answer that follows.
• Keep paragraphs short — 3 to 5 sentences maximum. Long, dense paragraphs are harder for LLMs to parse and extract cleanly. Short paragraphs create natural extraction boundaries.
• Use numbered lists for processes. Step-by-step processes in numbered lists are one of the most reliably cited content formats across all AI platforms.
• Use definition blocks. The pattern "Term: Definition." (or bold term followed by explanation) is highly AI-readable. It mirrors the training data format LLMs see most frequently for factual claims.
• Use tables for comparisons. Comparative data in HTML tables is exceptionally well-parsed by LLMs. Comparisons that would take three paragraphs to explain in prose can be extracted in a single table row.
• Add a TL;DR or summary box. A brief, scannable summary at the beginning or end of a section provides an ideal extraction target for AI systems generating short-form answers.

The direct answer format: lead with the conclusion

Traditional long-form SEO writing often builds towards a conclusion — context first, then explanation, then the actual answer. AI systems work in the opposite direction. They prefer content that states the direct answer first, then supports it with explanation and evidence.

This is sometimes called the inverted pyramid structure, borrowed from journalism. The most critical information appears at the top; supporting detail follows. Consider the difference between these two approaches to the question "What is a canonical tag?":

The right-hand version will be extracted and cited by AI systems far more reliably, because the answer is unambiguous and appears immediately. Apply this inverted pyramid principle to every H2 and H3 section on your page.

Information density and factual specificity

Information density refers to the ratio of useful facts and insights to total word count. Filler language, vague generalisations, and repetitive restatements of the same point all reduce information density — and LLMs are increasingly good at detecting and downweighting low-density content.

Factual specificity is equally important. Compare these two statements about page speed:

• Low specificity: "Slow pages can hurt your conversion rate significantly."
• High specificity: "Google's research shows that as page load time increases from 1 second to 3 seconds, the probability of a mobile user bouncing increases by 32%."

The specific version is more likely to be extracted and cited, because it contains a verifiable, attributable fact that the AI can include in its answer with confidence. Audit every paragraph of your content and ask: does this contain at least one specific, citable fact? If not, it is a candidate for revision or removal.

Schema markup for AI visibility

Structured data is the clearest possible signal you can send to both traditional search engines and AI systems about what your content means. While well-written prose can be interpreted by LLMs, schema markup provides explicit, machine-readable declarations of fact — no interpretation required.

The schema types with the strongest direct impact on AI citation likelihood are:

Implement all schema in JSON-LD format, placed in the <head> of each page. Validate every implementation using Google's Schema Markup Validator and Rich Results Test before deploying.

Building topical authority LLMs trust

A single, well-written article is rarely enough to secure consistent AI citations on a competitive topic. LLMs — particularly in retrieval-augmented generation (RAG) systems — assess not just the quality of an individual page but the authority of the domain on the relevant subject matter. A site that has published twenty high-quality, interlinked articles on technical SEO is treated as more authoritative on that topic than a site with one exceptional article surrounded by unrelated content.

The most effective approach is the topic cluster model: one comprehensive pillar page covering a broad topic (like this guide), supported by multiple focused cluster articles on specific sub-topics (canonical tags, robots.txt, Core Web Vitals, etc.), all interlinked. This architecture signals depth of coverage and helps AI systems understand that your site is a reliable, comprehensive resource on the subject.

To accelerate topical authority building, identify every question your target audience asks about your subject area — use tools like AnswerThePublic, Google's "People Also Ask" boxes, and Reddit threads to map the full question landscape. Then create dedicated, high-quality content that directly addresses each question. The goal is to own the topic, not just a keyword.

Mapping content to conversational query intent

AI search systems handle conversational, long-tail, and multi-part queries far more effectively than traditional search engines. Users of ChatGPT and Perplexity, in particular, tend to type full questions rather than two-word keyword strings. Your content must be written to match this conversational query style.

Practically, this means including natural-language question variants throughout your content — not just in headings, but in the body text itself. If your article covers the question "How do I find broken links on my site?", also ensure the text directly answers closely related variants: "What tool can I use to check for broken links?", "How does Google treat 404 errors?", and "What is the best way to fix broken internal links?" without forcing these as separate sections. LLMs retrieve at the paragraph level, so a single paragraph that naturally addresses a variant question can trigger a citation for that query.

Using citations and sources to signal trustworthiness

One of the clearest ways to signal to AI systems that your content is reliable is to cite your sources explicitly — just as an academic paper or reputable journalism outlet would. LLMs are trained on vast quantities of high-quality referenced text, and they recognise and favour the patterns of well-cited content.

Effective citation practices for GEO include:

• Link to primary sources. Link directly to the original study, official documentation, or primary data rather than to another blog post that summarises it. Primary source links are a strong trust signal.
• Name the source explicitly in your prose. "According to a 2025 Ahrefs study..." is more extractable and credible than "research shows..." The named attribution appears in the extracted content and raises its trustworthiness.
• Include original data where possible. Original surveys, experiments, and data sets are among the most frequently cited content types by LLMs, because they represent unique information unavailable elsewhere.
• Keep statistics current. Outdated statistics — particularly if the year is visible — actively undermine your credibility with AI systems that can cross-reference figures against more recent sources.

Brand mentions and entity recognition

Large language models understand the world through entities — named people, organisations, products, places, and concepts. If your brand, website, or authors are not recognised as entities within the models' understanding, you are at a significant disadvantage compared to established, entity-rich competitors.

Building entity recognition is a long-term process that involves: consistent use of your brand name across the web, Wikipedia presence (where eligible), Wikidata entries, Google Business Profile, and mentions in authoritative publications. The more your brand name appears in trusted external sources — news articles, industry directories, academic references — the more clearly it registers as a real, trustworthy entity in LLM training data.

For individual authors, building a recognisable author entity involves consistent bylines across publications, a verified Google author profile, and professional profiles on LinkedIn and relevant industry platforms. Author entities with strong external recognition are significantly more likely to have their work cited by AI systems.

Technical foundations that enable AI crawling

All GEO content strategy is undermined if AI crawlers cannot access and render your pages. The technical requirements for AI crawlability largely overlap with traditional technical SEO best practices, with a few important additions:

• Ensure your robots.txt does not block AI crawlers (unless you have made a deliberate decision to opt out — see the next section).
• Avoid JavaScript-only content rendering for information you want cited. AI crawlers vary in their JavaScript rendering capability; serve critical textual content in the initial HTML.
• Ensure fast server response times. AI crawlers may visit your site in bulk and move on quickly if your server is slow. A Time to First Byte (TTFB) under 200ms is the target.
• Keep your XML sitemap current. AI crawlers use sitemaps to discover fresh content efficiently, just as Googlebot does.
• Use semantic HTML. Tags like <article>, <section>, <main>, <h1>–<h6>, and <time> provide structural context that improves machine parsing accuracy.

AI crawlers, robots.txt, and the opt-out decision

Major AI companies operate named crawlers that respect robots.txt directives. You can block individual crawlers using the User-agent directive. The main AI crawlers and their user agent strings are:

The decision to block AI crawlers involves a genuine trade-off. Blocking training crawlers (like Google-Extended and GPTBot) may protect your content from being used in model training — but it does not necessarily prevent your content from being retrieved and cited in real-time AI answers (which use separate retrieval crawlers). Blocking PerplexityBot will prevent your site from appearing in Perplexity answers. Each site operator must make this decision based on their specific concerns around content licensing, data protection, and AI visibility strategy.

How to measure your GEO performance

GEO measurement is still an evolving discipline — the tools are less mature than those available for traditional SEO. However, a practical measurement framework can be assembled from existing data sources:

Google Search Console

Google Search Console now shows AI Overview impressions and clicks separately from standard organic results in the Performance report. Filter by "Search type: AI Overviews" to see which pages are appearing and how often. Monitor this data weekly for trending pages and for drops that may indicate your content has been deselected from an overview.

Referral traffic analysis

In Google Analytics 4 or any analytics platform, monitor referral traffic from known AI platforms: perplexity.ai, chat.openai.com, gemini.google.com, and others. Growing AI referral traffic is a direct indicator of GEO success. Segment by landing page to identify which content types are performing best.

Brand mention monitoring

Use tools like Brand24, Mention, or Google Alerts to track when your brand name or website is mentioned in AI-generated content that has been shared or published online. This provides a proxy measure of your AI citation frequency outside of direct referral tracking.

Manual AI query testing

Regularly query your target keywords directly in Google (with AI Overviews enabled), ChatGPT, and Perplexity. Record whether your content is cited, which competitors are cited instead, and what characteristics their cited content shares. This qualitative analysis frequently surfaces actionable optimisation opportunities that quantitative data misses.

Best content types for AI citation

Not all content formats are equally likely to be cited by AI systems. Based on observable citation patterns across Google AIO, Perplexity, and ChatGPT, the following content types consistently outperform:

• Definition and explainer articles. "What is X?" queries are heavily represented in AI Overviews. Clear, concise definitions with expanded context perform extremely well.
• Step-by-step how-to guides. Numbered process guides are among the most reliably extracted formats by AI systems. The explicit sequencing maps perfectly to how LLMs structure procedural answers.
• Comparison articles. "X vs Y" content, especially when presented in table format, is highly citeable for comparative queries. Ensure the comparison criteria are explicit and clearly labelled.
• Statistics and data roundups. Pages that aggregate current statistics with named sources are cited extremely frequently in AI answers, because they provide a dense, pre-aggregated source of citable facts.
• FAQ pages. Dedicated FAQ pages with FAQPage schema are structurally optimised for AI extraction by design. Every question-answer pair is a potential citation target.
• Original research and surveys. First-party data is the gold standard for AI citation. If you conduct original research — even small-scale surveys or experiments — publish the findings in a structured, clearly attributed format.
• Glossary and terminology pages. Domain-specific glossaries are heavily referenced by AI systems when constructing answers that require defining technical terms. These pages are low-competition, high-citation-frequency assets.

Common GEO mistakes to avoid

As with any evolving SEO discipline, the field is rife with misconceptions and counterproductive tactics. These are the most damaging GEO mistakes observed in practice:

Conclusion: GEO is a long game built on quality

Ranking in AI Overviews and being cited by LLMs is not about gaming a new algorithm — it is about demonstrating that your content is the most reliable, clearly structured, and authoritative answer to your audience's questions. The sites that will dominate AI-generated search results in 2026 and beyond are the ones investing now in original research, genuine expertise, and content that serves readers rather than crawlers.

Start by auditing your most important pages against the principles in this guide: do they lead with direct answers? Do they cite primary sources? Do they have appropriate schema markup? Do they demonstrate real-world experience with the topic? Close those gaps first, then layer in the advanced tactics — topical authority building, entity recognition, and platform-specific optimisation.

The transition to AI-mediated search is accelerating. The window to establish your site as a trusted, frequently cited source is open — but it will not remain open indefinitely. The brands that act now will build an AI citation advantage that compounds over time as their authority signals strengthen.