AI Search Optimization

TL;DR — AI searchAI search uses large language models and retrieval-augmented generation (RAG) to synthesize an answer from multiple sources rather than returning a ranked list of links. Examples include Google AI Overviews, ChatGPT Search, and Perplexity. optimization is getting your brand and content cited and represented accurately in AI answers — Google AI OverviewsAI Overviews are the AI-generated summary box Google shows above or within its regular search results, written by Gemini models from pages retrieved out of Google's normal Search index. It's a Search feature, not a separate platform or index., ChatGPT, Perplexity, Copilot. The good news: it’s mostly the same SEO you already do. Google itself says optimizing for AI searchAI search uses large language models and retrieval-augmented generation (RAG) to synthesize an answer from multiple sources rather than returning a ranked list of links. Examples include Google AI Overviews, ChatGPT Search, and Perplexity. “is still SEO.” Off-site references can contribute useful corroboration, but there is no universal rule that they matter exactly as much as on-site content.

What AI search optimization is

When you ask Google, ChatGPT, or Perplexity a question now, you often get a written answer instead of just a list of links. AI search optimizationAI search optimization is the practice of making your brand and content visible, citable, and accurately represented across AI-powered search — Google AI Overviews, ChatGPT, Perplexity, Copilot. It's built on traditional SEO plus a heavier emphasis on off-site brand mentions and content AI systems can cite. is the work of making sure your brand and pages are the ones those systems pull from, quote, and recommend — and that they describe you correctly.

You’ll see it called a lot of things: GEOGenerative Engine Optimization (GEO) is the practice of optimizing content and brand presence so AI-powered search engines and assistants — Google AI Overviews, ChatGPT, Perplexity — cite, recommend, or mention you when generating answers. Google's position is that it's still SEO. (Generative Engine Optimization), AEOAnswer Engine Optimization (AEO) is the practice of structuring content so engines deliver it as a direct answer — featured snippets, voice assistants, and AI search — rather than just a ranked link. Coined for voice search in 2018 and revived for the LLM era. Google's position is that it's still SEO. (Answer Engine Optimization), LLMO, “AI SEO.” Don’t let the acronyms intimidate you. They mostly describe the same handful of tactics with different labels on the box.

Why it matters (but not as much as the hype says)

AI search is growing fast and the traffic it sends converts unusually well — for In Ahrefs’ June 2025 internal study, AI traffic was about 0.5% of visitors but 12.1% of signups during the measured 30-day period. In a separate Ahrefs analysis of 76,000 sites, Google sent roughly 190x more website traffic than ChatGPT. So this is an emerging channel worth taking seriously, not a reason to abandon regular SEO.

My thesis: evolution, not revolution

Here’s the honest version. I gave a talk at Ahrefs Evolve 2025 called “GEOGenerative Engine Optimization — visibility inside AI answer engines.? AEO? LLMO? What’s With All This AI SEO Stuff?” and the one-line summary was a shift:

From “Optimize Your Site” → “Optimize How The Internet Talks About You.”

AI systems read the web to build their answers. The single biggest thing you can do is be the kind of brand the internet already talks about — in press, on Reddit, on YouTube, in trade coverage. Add good, helpful, well-sourced content on your own site, and you’ve covered most of it. That’s not a new discipline. It’s SEO, evolved.

Want the data, the correlations, and the map of the five sub-topics? Switch to the Advanced tab.

TL;DR — AI searchAI search uses large language models and retrieval-augmented generation (RAG) to synthesize an answer from multiple sources rather than returning a ranked list of links. Examples include Google AI Overviews, ChatGPT Search, and Perplexity. optimization is traditional SEO plus an off-site emphasis. The signal that changed: branded web mentions correlate with AI OverviewAI Overviews are the AI-generated summary box Google shows above or within its regular search results, written by Gemini models from pages retrieved out of Google's normal Search index. It's a Search feature, not a separate platform or index. appearances at 0.664, beating Domain Rating at 0.326 in Ahrefs’ 75,000-brand correlation study. On-page, the GEOGenerative Engine Optimization (GEO) is the practice of optimizing content and brand presence so AI-powered search engines and assistants — Google AI Overviews, ChatGPT, Perplexity — cite, recommend, or mention you when generating answers. Google's position is that it's still SEO. paper shows quotations (+41%), statistics (+33%), and cited sources (+30%) lift visibility while keyword stuffing hurts it (−9%). Two widely-sold “AI levers” are null results: llms.txtllms.txt is a proposed (not adopted) Markdown file at /llms.txt that gives AI systems a curated map of a site's most important pages. Proposed by Jeremy Howard in 2024, it's read mostly by coding agents like Claude Code — not search crawlers — and Google ignores it. (97% of files got zero requests) and adding schema to already-cited pages (no citation uplift). Both Google and Bing call it “still SEO.” This hub maps the five sub-topics — GEOGenerative Engine Optimization — visibility inside AI answer engines., AEOAnswer Engine Optimization (AEO) is the practice of structuring content so engines deliver it as a direct answer — featured snippets, voice assistants, and AI search — rather than just a ranked link. Coined for voice search in 2018 and revived for the LLM era. Google's position is that it's still SEO., entity SEOEntity SEO is the practice of helping search engines and AI systems clearly identify, classify, and trust the entities you represent — your brand, your people, your products — rather than just matching keyword strings. The goal is to be an unambiguous, well-corroborated entity in machine knowledge systems so AI can cite you with confidence., schema for AI, llmsA large language model (LLM) is a deep-learning model trained on massive text corpora to predict the next token and generate human-like text. LLMs use the transformer architecture and power AI search features like Google's AI Overviews (Gemini) and Bing Copilot (GPT-4)..txt — and the measurement tools that now exist.

What’s the same, and what’s actually different

Strip away the acronyms and most of AI search optimizationAI search optimization is the practice of making your brand and content visible, citable, and accurately represented across AI-powered search — Google AI Overviews, ChatGPT, Perplexity, Copilot. It's built on traditional SEO plus a heavier emphasis on off-site brand mentions and content AI systems can cite. is the SEO you already do. Google is explicit: “From Google Search’s perspective, optimizing for generative AI searchAI search uses large language models and retrieval-augmented generation (RAG) to synthesize an answer from multiple sources rather than returning a ranked list of links. Examples include Google AI Overviews, ChatGPT Search, and Perplexity. is optimizing for the search experience, and thus still SEO.” AI OverviewsAI Overviews are the AI-generated summary box Google shows above or within its regular search results, written by Gemini models from pages retrieved out of Google's normal Search index. It's a Search feature, not a separate platform or index. and AI Mode are “rooted in our core Search ranking and quality systems” — they retrieve from the same indexStoring a crawled page in the search index so it can appear in results. Crawled is not the same as indexed — Google selects what to keep, and indexing isn't guaranteed., using RAG and query fan-outQuery fan-out is the technique where an AI search system breaks a single user question into multiple related sub-queries, runs those searches concurrently, and synthesizes the retrieved results into one answer. Google confirms AI Overviews and AI Mode 'may use a query fan-out technique' issuing multiple related searches across subtopics., so if you can’t rank, you can’t be retrieved. Traditional SEO eligibility gates AI eligibility. That’s the spine of my “evolution, not revolution” framing.

CrawlerA crawler — also called a spider or bot — is an automated program that fetches web pages, extracts their links, and queues new URLs to visit. Search engines use crawlers to discover and download content for their index. permission is a separate gate from ranking eligibility, and it’s provider-specific: GooglebotGooglebot is Google's web crawler — the software that fetches pages so Google can index and rank them. It comes in two variants, Googlebot Smartphone (primary, under mobile-first indexing) and Googlebot Desktop, and runs an evergreen Chromium renderer., OpenAI’s OAI-SearchBot/GPTBot/ChatGPT-User, and other bots each have their own documented purpose and robots.txt controls. Allowing (or blocking) a named crawler tells you about access, not whether that provider indexed, retrieved, cited, or even fetched a given page recently — see AI CrawlersAI crawlers are bots from AI companies that fetch web pages to train language models, build AI-search indexes, or answer live user questions. They come in three categories, each with its own user-agent tokens and its own robots.txt controls. for the per-bot breakdown and controls.

What is different is the weighting. Two things matter more than they did:

1. Off-site brand presence — what the rest of the web says about you.
2. Passage-level citability — whether a specific paragraph is quotable as an answer.

The signal that changed: brand mentions over backlinks

This is the most important — and most actionable — finding in the whole cluster. In Ahrefs’ correlation study of 75,000 brands (filtered to Domain Rating > 40 with a keyword at 800+ monthly search volume, then checked for mentions in Google AI Overviews via Brand Radar), here’s how four signals correlated with brand appearances in Google AI Overviews:

Unlinked brand mentions — which pass no PageRankPageRank is Google's original recursive link-graph algorithm: a page's score depends on the scores of the pages linking to it, and in the published model each page's score is split across its outbound links (the simplified version: links are weighted votes). Google says it's evolved since launch but still part of its core ranking systems. and barely register in traditional SEO — are the strongest correlate of appearing in Google AI Overviews in this 75,000-brand dataset. Domain Rating, the classic authority proxy, comes in last. As I put it: “It shouldn’t come as a surprise that prominent brands are mentioned on pages that website owners deemed worthy enough to link to.” Standard caveat applies — correlation isn’t causation. This is an observed association in one sampled dataset, not a disclosed algorithmic weight, and it’s specific to Google AI Overviews — Ahrefs hasn’t published the equivalent breakdown for ChatGPT or Perplexity. Treat optimize how the internet talks about you as a testable hypothesis worth budget, not a proven lever: set a baseline before you reallocate spend on the strength of it.

What the research says works (and what doesn’t)

The academic anchor is the GEO paper (Aggarwal et al., Princeton / IIT Delhi, KDD 2024). They tested content tactics against AI visibility:

Two takeaways. First, keyword stuffing actively hurts — write naturally. Second, lower-ranked pages benefit disproportionately: a rank-5 page citing sources saw +115.1% while a rank-1 page saw −30.3%, because AI conditions on passage quality, not pure backlink authority. AI optimization may matter more for challengers than incumbents. And from my own data, original research is the moat — 5 of Ahrefs’ top 6 AI-visible posts last year were data studies.

These are the GEO paper’s own effect sizes within its evaluation setup — a fixed set of engines, queries, and content interventions from 2024. Read them as evidence that citable, well-sourced writing helps, not as a percentage lift guaranteed on today’s commercial models or your own traffic.

What does NOT work

I’ll name the null results as clearly as the wins:

• llms.txtllms.txt is a proposed (not adopted) Markdown file at /llms.txt that gives AI systems a curated map of a site's most important pages. Proposed by Jeremy Howard in 2024, it's read mostly by coding agents like Claude Code — not search crawlers — and Google ignores it. for AI search citations. In our study of 137,210 domains, 97% of published llms.txt files got zero requests in May 2026. Google explicitly ignores it. It’s useful for developer-doc sites consumed by coding agents — not for AI search visibility.
• Bolting schema onto already-cited pages. Our controlled study of 1,885 pages adding schema found no meaningful citation uplift on any platform (Google AI Overviews −4.6%, AI Mode +2.4%, ChatGPT +2.2% — all indistinguishable from zero). Schema is still worth it for entity recognition; it is not a citation lever for pages already in the consideration set.
• ChunkingChunking is splitting a document into smaller passages so AI systems can embed, index, and retrieve the single most relevant piece — not the whole page — in response to a query. It's a foundational step in RAG pipelines and the conceptual cousin of Google's passage ranking. or rewriting content “for AI.” Google: you don’t need to write in a special way for generative AI search.

The five areas of AI search optimization

This hub is the map. Each area is its own deep dive (they auto-link as siblings publish):

• Generative Engine Optimization (GEO) — the broadest sub-topic: on-site content tactics (quotations, stats, source citations) and off-site brand presence. The brand-mentions-beat-DR finding lives here.
• Answer Engine Optimization (AEO) — structuring content so an engine extracts it as the direct answer. Coined by Jason Barnard in January 2018 around voice search and featured snippets, revived for the AI era. In practice it overlaps heavily with GEO.
• Entity SEOEntity SEO is the practice of helping search engines and AI systems clearly identify, classify, and trust the entities you represent — your brand, your people, your products — rather than just matching keyword strings. The goal is to be an unambiguous, well-corroborated entity in machine knowledge systems so AI can cite you with confidence. for AI — building clear, cross-referenced entity signals (Organization/Person schema with sameAs to Wikidata, Wikipedia, LinkedIn) so knowledge graphsThe Knowledge Graph is Google's database of entities — people, places, organizations, and things — and the factual relationships between them. It's separate from any single website's structured data: your schema markup is one of many possible inputs to the graph, not the graph itself. identify you correctly and AI hallucinates less about your brand.
• Schema markup for AISchema markup (structured data) is machine-readable code — usually JSON-LD — that labels what your content means using the schema.org vocabulary. For AI search it's infrastructure for entity disambiguation, not a direct citation lever: controlled studies found no meaningful uplift in AI citations from adding it. — the technical, machine-readable layer. Honest framing: no special schema is required for AI Overviews, and adding it to already-cited pages didn’t move citations — but Organization + sameAs is the clearest schema → Knowledge Graph → AI path, and Microsoft has confirmed Bing’s LLMs use schema.
• llms.txt — what it is, and (importantly) what it isn’t. Useful for developer docs; not a meaningful AI-search lever.

Where to go next

Pick the sub-topic that matches your need: generative engine optimizationGenerative Engine Optimization (GEO) is the practice of optimizing content and brand presence so AI-powered search engines and assistants — Google AI Overviews, ChatGPT, Perplexity — cite, recommend, or mention you when generating answers. Google's position is that it's still SEO., answer engine optimizationAnswer Engine Optimization (AEO) is the practice of structuring content so engines deliver it as a direct answer — featured snippets, voice assistants, and AI search — rather than just a ranked link. Coined for voice search in 2018 and revived for the LLM era. Google's position is that it's still SEO., entity SEOEntity SEO is the practice of helping search engines and AI systems clearly identify, classify, and trust the entities you represent — your brand, your people, your products — rather than just matching keyword strings. The goal is to be an unambiguous, well-corroborated entity in machine knowledge systems so AI can cite you with confidence., schema markup for AISchema markup (structured data) is machine-readable code — usually JSON-LD — that labels what your content means using the schema.org vocabulary. For AI search it's infrastructure for entity disambiguation, not a direct citation lever: controlled studies found no meaningful uplift in AI citations from adding it., llms.txtllms.txt is a proposed (not adopted) Markdown file at /llms.txt that gives AI systems a curated map of a site's most important pages. Proposed by Jeremy Howard in 2024, it's read mostly by coding agents like Claude Code — not search crawlers — and Google ignores it., and AI Overviews optimizationAI Overviews optimization is the practice of getting your pages cited and represented in Google's AI Overviews — the Gemini-generated summary box in Google's own search results. Google says there are no special optimizations for it: the same index, ranking, and quality systems decide eligibility, so it's still SEO. for the box in Google’s own SERPs specifically. For the bigger picture of how these systems work, see AI SearchAI search uses large language models and retrieval-augmented generation (RAG) to synthesize an answer from multiple sources rather than returning a ranked list of links. Examples include Google AI Overviews, ChatGPT Search, and Perplexity..

Measurement: how to actually track this

The tooling finally exists:

• Google Search ConsoleA free Google service that reports how a site performs in Google Search and surfaces problems with how Google crawls, indexes, and serves it. It's first-party data straight from Google — but you don't need it to appear in results. — sites appearing in AI Overviews or AI Mode are folded into the “Web” search type in the Performance report, not broken out as a separate, filterable AI-features row — so GSC alone can’t isolate AI Overview impressions from ordinary blue-link ones.
• Bing Webmaster ToolsMicrosoft's free portal for monitoring and improving how a site appears in Bing search — the peer to Google Search Console, plus IndexNow instant indexing, richer backlink data, and keyword volumes. Because Bing's index also feeds Microsoft Copilot, it doubles as a window into AI-search visibility. — AI Performance dashboard (public preview, Feb 2026) — the first time a search engine reports your AI citation data directly: total citations, groundingGrounding is anchoring an AI model's answer to source documents it retrieves at the moment you ask — not to the patterns frozen into its weights during training. Retrieval-Augmented Generation (RAG) is the most common way to do it. queries, and page-level activity.
• Ahrefs Brand Radar — share of voice across AI platforms.
• Web analytics — segment by referrer to see visits from ChatGPT, Perplexity, Gemini.

One reality check before you over-invest: only 7 of the top 50 most-cited domains appear across all three major platforms. “Ranking for AI” isn’t a single, monolithic goal — each engine has its own source preferences.

AI summary

A condensed take on the Advanced version:

• AI searchAI search uses large language models and retrieval-augmented generation (RAG) to synthesize an answer from multiple sources rather than returning a ranked list of links. Examples include Google AI Overviews, ChatGPT Search, and Perplexity. optimization = traditional SEO + an off-site emphasis. Google and Bing both call it “still SEO”; AI OverviewsAI Overviews are the AI-generated summary box Google shows above or within its regular search results, written by Gemini models from pages retrieved out of Google's normal Search index. It's a Search feature, not a separate platform or index. retrieve from the core ranking systems, so SEO eligibility gates AI eligibility. CrawlerA crawler — also called a spider or bot — is an automated program that fetches web pages, extracts their links, and queues new URLs to visit. Search engines use crawlers to discover and download content for their index. permission (GooglebotGooglebot is Google's web crawler — the software that fetches pages so Google can index and rank them. It comes in two variants, Googlebot Smartphone (primary, under mobile-first indexing) and Googlebot Desktop, and runs an evergreen Chromium renderer., OAI-SearchBot/GPTBot/ChatGPT-User) is a separate, provider-specific gate — access isn’t proof of indexingStoring a crawled page in the search index so it can appear in results. Crawled is not the same as indexed — Google selects what to keep, and indexing isn't guaranteed., retrieval, or citation.
• The signal that changed: in Ahrefs’ 75,000-brand study, branded web mentions correlate with Google AI Overview appearances at 0.664 — stronger than Domain Rating (0.326). It’s a correlation in one dataset, not a disclosed ranking weight — treat “optimize how the internet talks about you” as a hypothesis to test.
• On-page wins (GEOGenerative Engine Optimization (GEO) is the practice of optimizing content and brand presence so AI-powered search engines and assistants — Google AI Overviews, ChatGPT, Perplexity — cite, recommend, or mention you when generating answers. Google's position is that it's still SEO. paper’s own experiment): quotations +41%, statistics +33%, cited sources +30% (and +115.1% for rank-5 pages). Keyword stuffing −9% — it hurts.
• Original research is the moat: 5 of Ahrefs’ top 6 AI-visible posts were data studies.
• Null results: llms.txtllms.txt is a proposed (not adopted) Markdown file at /llms.txt that gives AI systems a curated map of a site's most important pages. Proposed by Jeremy Howard in 2024, it's read mostly by coding agents like Claude Code — not search crawlers — and Google ignores it. (97% of files got zero requests) and adding schema to already-cited pages (no uplift) are not AI-search levers.
• Five sub-topics: GEOGenerative Engine Optimization — visibility inside AI answer engines., AEOAnswer Engine Optimization (AEO) is the practice of structuring content so engines deliver it as a direct answer — featured snippets, voice assistants, and AI search — rather than just a ranked link. Coined for voice search in 2018 and revived for the LLM era. Google's position is that it's still SEO., entity SEOEntity SEO is the practice of helping search engines and AI systems clearly identify, classify, and trust the entities you represent — your brand, your people, your products — rather than just matching keyword strings. The goal is to be an unambiguous, well-corroborated entity in machine knowledge systems so AI can cite you with confidence., schema for AI, llms.txtllms.txt is a proposed (not adopted) Markdown file at /llms.txt that gives AI systems a curated map of a site's most important pages. Proposed by Jeremy Howard in 2024, it's read mostly by coding agents like Claude Code — not search crawlers — and Google ignores it..
• Measurement now exists: GSC (AI-feature traffic sits inside the “Web” search type, not a separate filter), Bing’s AI Performance dashboard (Feb 2026), Brand Radar, referrer-segmented analytics. Only 7 of the top 50 cited domains appear on all three platforms.

Official documentation

Primary-source guidance from the search engines on AI searchAI search uses large language models and retrieval-augmented generation (RAG) to synthesize an answer from multiple sources rather than returning a ranked list of links. Examples include Google AI Overviews, ChatGPT Search, and Perplexity..

Google

• AI features and your website — how AI OverviewsAI Overviews are the AI-generated summary box Google shows above or within its regular search results, written by Gemini models from pages retrieved out of Google's normal Search index. It's a Search feature, not a separate platform or index. and AI Mode use your existing SEO signals, and what’s not required.
• How AI features in Google Search work / optimization guidance — the “still SEO” position and the things you don’t need to do.
• Structured data introduction — the schema baseline (note: no special AI schema is required).

Bing / Microsoft

• Introducing AI Performance in Bing Webmaster Tools (Feb 2026) — the new citations / groundingGrounding is anchoring an AI model's answer to source documents it retrieves at the moment you ask — not to the patterns frozen into its weights during training. Retrieval-Augmented Generation (RAG) is the most common way to do it.-queries / cited-pages dashboard, and Bing’s optimization recommendations.
• Generative answers and public websites (Copilot Studio) — how Copilot grounds answers in the Bing indexStoring a crawled page in the search index so it can appear in results. Crawled is not the same as indexed — Google selects what to keep, and indexing isn't guaranteed..

Quotes from the source

On-the-record statements from Google, Bing, and my own talk.

Google — “it’s still SEO”

• “From Google Search’s perspective, optimizing for generative AI searchAI search uses large language models and retrieval-augmented generation (RAG) to synthesize an answer from multiple sources rather than returning a ranked list of links. Examples include Google AI Overviews, ChatGPT Search, and Perplexity. is optimizing for the search experience, and thus still SEO.”

• “The best practices for SEO continue to be relevant because our generative AI features on Google Search are rooted in our core Search ranking and quality systems.”

• “There are no additional requirements to appear in AI OverviewsAI Overviews are the AI-generated summary box Google shows above or within its regular search results, written by Gemini models from pages retrieved out of Google's normal Search index. It's a Search feature, not a separate platform or index. or AI Mode, nor other special optimizations necessary.” — Google Search Central, AI features documentation.

• “Good SEO is good GEOGenerative Engine Optimization (GEO) is the practice of optimizing content and brand presence so AI-powered search engines and assistants — Google AI Overviews, ChatGPT, Perplexity — cite, recommend, or mention you when generating answers. Google's position is that it's still SEO..” — Danny Sullivan, Google Search Liaison, via Search Engine Land (November 2025).

Bing / Microsoft — make sure you’re indexedStoring a crawled page in the search index so it can appear in results. Crawled is not the same as indexed — Google selects what to keep, and indexing isn't guaranteed., then win the citation

• “Getting cited comes down to two things: first, make sure Bing has indexed your content so it appears in result sets, and second, make sure your content is clear, structured, and trustworthy enough to win the citation over competitors.”
• “Clear headings, tables, and FAQ sections help surface key information and make content easier for AI systems to reference accurately.” — Bing WebmasterMicrosoft's free portal for monitoring and improving how a site appears in Bing search — the peer to Google Search Console, plus IndexNow instant indexing, richer backlink data, and keyword volumes. Because Bing's index also feeds Microsoft Copilot, it doubles as a window into AI-search visibility. Blog, Introducing AI Performance (February 2026).

Me — Ahrefs Evolve 2025

• “From ‘Optimize Your Site’ → ‘Optimize How The Internet Talks About You’.” — my talk, GEOGenerative Engine Optimization — visibility inside AI answer engines.? AEOAnswer Engine Optimization (AEO) is the practice of structuring content so engines deliver it as a direct answer — featured snippets, voice assistants, and AI search — rather than just a ranked link. Coined for voice search in 2018 and revived for the LLM era. Google's position is that it's still SEO.? LLMO? What’s With All This AI SEO Stuff? (slides, video), October 15, 2025.

AI search optimization checklist

Do the SEO baseline first — it gates AI eligibility — then layer the AI-specific work.

Baseline SEO (this is most of the job)

• Important pages are crawlable, indexable, and actually rank (RAGRAG is the retrieve-then-generate pattern behind AI search: the system retrieves relevant passages from an external index at query time, injects them into the model's context, and generates an answer grounded in those sources — without changing the model's weights. can only retrieve what’s eligible).
• Content is genuinely helpful, people-first, and brings unique points of view / first-hand experience.
• Clear heading hierarchy; tables and FAQ sections where they fit (Bing surfaces these).
• Organization + sameAs schema connecting you to Wikidata / Wikipedia / LinkedIn for entity recognition.

AI-specific layer

• Build authentic branded web mentions — PR, trade coverage, analyst reports, Reddit, YouTube (the 0.664 correlation). Treat this as a hypothesis to test against your own baseline, not a guaranteed lever.
• Add quotations from recognized authorities to key articles (+41%).
• Back claims with statistics and data (+33–40%).
• Cite authoritative sources inline (+30%; biggest lift for lower-ranked pages).
• Invest in original research / data studies — the most defensible AI-visible format.
• Keep important content fresh (AI cites material ~25.7% fresher than organic).
• Set up measurement: GSC Performance reportThe Google Search Console report that shows how your site actually performed in Google Search, built from real impressions and clicks. It reports four metrics — clicks, impressions, average CTR, and average position — and keeps the most recent 16 months of data. (AI-feature traffic is folded into the “Web” search type, not a separate filter), Bing AI Performance dashboard, Brand Radar, referrer-segmented analytics.

Don’t bother (or actively avoid)

• Keyword stuffing (−9% — it hurts).
• llms.txtllms.txt is a proposed (not adopted) Markdown file at /llms.txt that gives AI systems a curated map of a site's most important pages. Proposed by Jeremy Howard in 2024, it's read mostly by coding agents like Claude Code — not search crawlers — and Google ignores it. for AI search citationsAn AI citation is the visible source link an AI answer engine shows next to its generated text — the clickable reference that credits the web page it used. A citation's presence is a separate thing from whether the cited page actually supports the statement, and from being retrieved (read behind the scenes) or merely mentioned (named without a link); citation is driven more by brand mentions and being retrievable than by traditional ranking. (97% get zero requests; Google ignores it).
• Adding schema purely to boost citations on already-cited pages (no uplift).
• Rewriting/chunkingChunking is splitting a document into smaller passages so AI systems can embed, index, and retrieve the single most relevant piece — not the whole page — in response to a query. It's a foundational step in RAG pipelines and the conceptual cousin of Google's passage ranking. content “for AI” (Google says it’s unnecessary).

The prioritization framework

A simple order of operations so you spend effort where the evidence is.

First — earn the baseline (it’s the gate). Be crawlable, indexable, and rankable; ship genuinely helpful, original content with clear structure. AI OverviewsAI Overviews are the AI-generated summary box Google shows above or within its regular search results, written by Gemini models from pages retrieved out of Google's normal Search index. It's a Search feature, not a separate platform or index. retrieve from the core ranking systems, so nothing downstream works until this is true. This is ~80% of the job and it’s just SEO.

Second — test how the internet talks about you (strongest correlate foundA 302 (\"Found\") is a temporary redirect: it forwards users to a new URL while telling search engines the original URL should stay in the index. It's a weak canonicalization signal, not the zero-equity dead end of SEO folklore.). In one Ahrefs dataset of 75,000 brands, branded web mentions (0.664) beat Domain Rating (0.326) as a correlate of appearing in Google AI OverviewsAI Overviews are the AI-generated summary box Google shows above or within its regular search results, written by Gemini models from pages retrieved out of Google's normal Search index. It's a Search feature, not a separate platform or index. — not a disclosed ranking weight. It’s worth trialing earned coverage, PR, community presence (Reddit, YouTube), and authentic mentions against a baseline. This is the part that’s genuinely different from classic on-site SEO, and where the correlational evidence points furthest — treat it as a hypothesis, not a guarantee.

Third — make individual passages citable. Add quotations (+41%), statistics (+33%), and source citations (+30%). Think of it as featured-snippet writing applied to many paragraphs, not just one. Lower-ranked pages gain the most here.

Fourth — establish your entity. Organization + sameAs schema, consistent name/logo/description everywhere, Wikidata/Wikipedia where applicable. The clearest place structured dataStructured data is a standardized way of labeling page content (using the schema.org vocabulary in JSON-LD, Microdata, or RDFa) so search engines can understand its meaning. It's not a direct ranking factor — its value is rich results and entity understanding. demonstrably affects AI behavior (entity resolution, fewer hallucinationsAn AI hallucination is when a large language model generates output that is confidently stated but factually wrong, made up, or unsupported by its source. It's a side effect of next-token prediction — not a bug that can be fully eliminated.).

What to skip. llms.txtllms.txt is a proposed (not adopted) Markdown file at /llms.txt that gives AI systems a curated map of a site's most important pages. Proposed by Jeremy Howard in 2024, it's read mostly by coding agents like Claude Code — not search crawlers — and Google ignores it. for citations, schema-for-citations on already-cited pages, keyword stuffing, and AI-specific rewriting. Spending here is motion, not progress.

The mental model: SEO-first, GEOGenerative Engine Optimization (GEO) is the practice of optimizing content and brand presence so AI-powered search engines and assistants — Google AI Overviews, ChatGPT, Perplexity — cite, recommend, or mention you when generating answers. Google's position is that it's still SEO.-adjacent. AI searchAI search uses large language models and retrieval-augmented generation (RAG) to synthesize an answer from multiple sources rather than returning a ranked list of links. Examples include Google AI Overviews, ChatGPT Search, and Perplexity. optimization is a layer on top of good SEO, not a replacement for it — evolution, not revolution.

AI search optimization mistakes

Buying an AI-only shortcut before fixing SEO

Generated answers still depend on accessible, useful, trustworthy sources. Repair indexability, content quality, and authority before chasing a format-specific tactic.

Measuring one prompt as a market

Answers vary by wording, engine, location, and time. Use a stable portfolio of real audience questions and repeat it consistently.

Confusing a brand mention with a source citation

Both matter, but they are different outcomes. Track whether the brand appears, whether the site is cited, and whether the answer represents the entity accurately.

Publishing evidence-shaped filler

Unsupported statistics, quotations, and synthetic examples do not become trustworthy because AI systems favor clear evidence. Use verifiable sources and disclose methods.

Tools for AI search optimization

• AI Search Readiness Report: Review whether a page exposes accessible, quotable, entity-clear source material before testing visibility.
• Quotability & Entity-Preserving Rewriter: Tighten passages while retaining named entities and factual boundaries; verify every output against the source.
• Entity Coverage Analyzer: Find missing or ambiguous entities and relationships that can make an answer harder to ground.
• Search ConsoleA free Google service that reports how a site performs in Google Search and surfaces problems with how Google crawls, indexes, and serves it. It's first-party data straight from Google — but you don't need it to appear in results. and analytics: Keep blue-link demand, clicks, landing-page outcomes, and identifiable AI referrals in the same measurement view.
• A versioned prompt tracker: Store the query, engine, date, answer, mention, citation, cited URL, and accuracy judgment so observations are reproducible.

Measure AI search optimization

Brand answer visibility

Metric: Tracked prompts where the brand appears. What it tells you: Whether the entity enters relevant generated answers. How to pull it: Repeat a fixed prompt set by engine and store outputs. Benchmark / realistic range: Build a baseline by topic and system; no universal target survives model variance. Cadence: Monthly.

Direct citation rate

Metric: Eligible answers that cite the site’s URL. What it tells you: Whether the site is used as a source rather than merely mentioned. How to pull it: Extract and classify links from saved answers. Benchmark / realistic range: Compare with your prior period and the same competitors on the same prompts. Cadence: Monthly.

Representation accuracy

Metric: Answers containing no material factual error about the tracked brand, product, or person. What it tells you: Whether increased visibility is actually useful. How to pull it: Human review against a maintained fact sheet and cited sources. Benchmark / realistic range: Zero known material errors is the goal; track minor and material errors separately. Cadence: Monthly and after major brand facts change.

AI-referred outcomes

Metric: Identifiable AI referral visits and the conversions or assisted outcomes they produce. What it tells you: The measurable business value after an answer. How to pull it: Analytics referral grouping and destination events. Benchmark / realistic range: Use the site’s baseline because many referrals are missing or stripped. Cadence: Monthly.

Test yourself: AI Search Optimization

Resources worth your time

My research (Ahrefs)

• Brand mentions vs. backlinks correlation study (75,000 brands) — the 0.664 vs. 0.326 finding.
• GEO: how brands grow presence in AI answers — practitioner strategy piece.
• Does being mentioned on highly-linked pages influence AI mentions?
• Insights from 56M AI Overviews — reach, CTR impact, query mix.
• AI search traffic & conversions — the 0.5% visitors → 12.1% signups data.
• ChatGPT has ~12% of Google’s search volume — the 190x traffic gap.
• We tracked 1,885 pages adding schema — citations barely moved.
• The llms.txt study (137K domains) — 97% got zero requests.

My speaking

• GEOGenerative Engine Optimization (GEO) is the practice of optimizing content and brand presence so AI-powered search engines and assistants — Google AI Overviews, ChatGPT, Perplexity — cite, recommend, or mention you when generating answers. Google's position is that it's still SEO.? AEOAnswer Engine Optimization (AEO) is the practice of structuring content so engines deliver it as a direct answer — featured snippets, voice assistants, and AI search — rather than just a ranked link. Coined for voice search in 2018 and revived for the LLM era. Google's position is that it's still SEO.? LLMO? What’s With All This AI SEO Stuff? — Ahrefs Evolve 2025 (slides · video). My standing disclaimer applies: this is my understanding of these systems, not a guarantee it’s 100% complete or accurate.

From others

• Ryan Law, GEO, LLMO, AEO… It’s All Just SEO (Ahrefs).
• Aggarwal et al., GEO: Generative Engine Optimization (KDD 2024, Princeton / IIT Delhi).
• Google Search Central, AI features and your website — the primary-source “still SEO” stance and what’s NOT required.
• Google Search Central, AI optimization guide — official guidance on optimizing for generative AI searchAI search uses large language models and retrieval-augmented generation (RAG) to synthesize an answer from multiple sources rather than returning a ranked list of links. Examples include Google AI Overviews, ChatGPT Search, and Perplexity..
• Bing WebmasterMicrosoft's free portal for monitoring and improving how a site appears in Bing search — the peer to Google Search Console, plus IndexNow instant indexing, richer backlink data, and keyword volumes. Because Bing's index also feeds Microsoft Copilot, it doubles as a window into AI-search visibility. Blog, Introducing AI Performance in Bing Webmaster Tools — Bing’s own framing of citation signals and the new measurement dashboard.
• Search Engine Land — AI Overviews coverage — news and analysis on Google’s AI searchAI search uses large language models and retrieval-augmented generation (RAG) to synthesize an answer from multiple sources rather than returning a ranked list of links. Examples include Google AI Overviews, ChatGPT Search, and Perplexity. rollout and emerging optimization practices.
• r/TechSEO on Reddit — community discussion of real-world AI search tests, llms.txtllms.txt is a proposed (not adopted) Markdown file at /llms.txt that gives AI systems a curated map of a site's most important pages. Proposed by Jeremy Howard in 2024, it's read mostly by coding agents like Claude Code — not search crawlers — and Google ignores it. experiments, and brand-mention strategies.

Stats worth citing

• Brand mentions beat backlinks (one dataset). Branded web mentions correlate with Google AI OverviewAI Overviews are the AI-generated summary box Google shows above or within its regular search results, written by Gemini models from pages retrieved out of Google's normal Search index. It's a Search feature, not a separate platform or index. appearances at 0.664, vs. 0.326 for Domain Rating, in Ahrefs’ study of 75,000 brands — the strongest correlate foundA 302 (\"Found\") is a temporary redirect: it forwards users to a new URL while telling search engines the original URL should stay in the index. It's a weak canonicalization signal, not the zero-equity dead end of SEO folklore., not a disclosed ranking weight. Source
• GEOGenerative Engine Optimization (GEO) is the practice of optimizing content and brand presence so AI-powered search engines and assistants — Google AI Overviews, ChatGPT, Perplexity — cite, recommend, or mention you when generating answers. Google's position is that it's still SEO. content tactics: quotations +41%, statistics +33%, cited sources +30% (and +115.1% for rank-5 pages); keyword stuffing −9%. Source
• llms.txtllms.txt is a proposed (not adopted) Markdown file at /llms.txt that gives AI systems a curated map of a site's most important pages. Proposed by Jeremy Howard in 2024, it's read mostly by coding agents like Claude Code — not search crawlers — and Google ignores it. is a null result for AI searchAI search uses large language models and retrieval-augmented generation (RAG) to synthesize an answer from multiple sources rather than returning a ranked list of links. Examples include Google AI Overviews, ChatGPT Search, and Perplexity.. 97% of published llmsA large language model (LLM) is a deep-learning model trained on massive text corpora to predict the next token and generate human-like text. LLMs use the transformer architecture and power AI search features like Google's AI Overviews (Gemini) and Bing Copilot (GPT-4)..txt files received zero requests in May 2026 (137,210 domains). Source
• Schema didn’t move citations. Across 1,885 pages adding schema, no meaningful uplift on any AI platform (Google AI OverviewsAI Overviews are the AI-generated summary box Google shows above or within its regular search results, written by Gemini models from pages retrieved out of Google's normal Search index. It's a Search feature, not a separate platform or index. −4.6%, AI Mode +2.4%, ChatGPT +2.2%). Source
• AI traffic is small but high-intent. For Ahrefs, AI traffic = 0.5% of visitors but 12.1% of signups — a ~23x higher conversion rate than organic. Source
• Google still dwarfs AI searchAI search uses large language models and retrieval-augmented generation (RAG) to synthesize an answer from multiple sources rather than returning a ranked list of links. Examples include Google AI Overviews, ChatGPT Search, and Perplexity.. Google sends roughly 190x more traffic than ChatGPT, whose CTR is ~96% lower. Source
• Each platform is different. Only 7 of the top 50 most-cited domains appear across all three major AI platforms (Google AIOs, ChatGPT, Perplexity).

AI Search Optimization

AI search optimization is the practice of making your brand and content visible, citable, and accurately represented across AI-powered search — Google AI Overviews, ChatGPT, Perplexity, Copilot. It's built on traditional SEO plus a heavier emphasis on off-site brand mentions and content AI systems can cite.

Related: GEO, AI Search, Schema Markup for AI, Entity SEO

AI Search Optimization

AI search optimizationAI search optimization is the practice of making your brand and content visible, citable, and accurately represented across AI-powered search — Google AI Overviews, ChatGPT, Perplexity, Copilot. It's built on traditional SEO plus a heavier emphasis on off-site brand mentions and content AI systems can cite. is the umbrella practice of ensuring your brand, content, and website are retrieved, cited, and accurately represented when AI systems synthesize answers — across Google AI OverviewsAI Overviews are the AI-generated summary box Google shows above or within its regular search results, written by Gemini models from pages retrieved out of Google's normal Search index. It's a Search feature, not a separate platform or index. and AI Mode, ChatGPT, Perplexity, and Microsoft Copilot. It draws on the same fundamentals as traditional SEO (crawlabilityCrawlability is how well search engine crawlers can discover, access, and fetch a site's pages. A crawlability issue is any technical condition — blocked access, broken links, server failures, or bloated URL inventory — that stops pages from reaching the index., content quality, authority) and adds a layer focused on off-site brand presence, passage-level citability, and entity clarity.

The field collects several overlapping labels — GEOGenerative Engine Optimization (GEO) is the practice of optimizing content and brand presence so AI-powered search engines and assistants — Google AI Overviews, ChatGPT, Perplexity — cite, recommend, or mention you when generating answers. Google's position is that it's still SEO. (Generative Engine Optimization), AEOAnswer Engine Optimization (AEO) is the practice of structuring content so engines deliver it as a direct answer — featured snippets, voice assistants, and AI search — rather than just a ranked link. Coined for voice search in 2018 and revived for the LLM era. Google's position is that it's still SEO. (Answer Engine Optimization), LLMO (Large Language ModelA large language model (LLM) is a deep-learning model trained on massive text corpora to predict the next token and generate human-like text. LLMs use the transformer architecture and power AI search features like Google's AI Overviews (Gemini) and Bing Copilot (GPT-4). Optimization), and entity SEOEntity SEO is the practice of helping search engines and AI systems clearly identify, classify, and trust the entities you represent — your brand, your people, your products — rather than just matching keyword strings. The goal is to be an unambiguous, well-corroborated entity in machine knowledge systems so AI can cite you with confidence. — but the core strategic shift is one thing: moving from optimize your site to optimize how the internet talks about you. Both Google and Bing officially frame it as still SEO; AI features at Google are “rooted in our core Search ranking and quality systems,” so traditional SEO eligibility gates AI eligibility.

What’s genuinely different is the signal hierarchy. In my Ahrefs research, branded web mentions correlate with AI Overview appearances at 0.664 — stronger than Domain Rating at 0.326. Unlinked mentions, which pass no PageRankPageRank is Google's original recursive link-graph algorithm: a page's score depends on the scores of the pages linking to it, and in the published model each page's score is split across its outbound links (the simplified version: links are weighted votes). Google says it's evolved since launch but still part of its core ranking systems., turn out to be the best predictor of showing up in AI answers. On-page, the academic GEOGenerative Engine Optimization — visibility inside AI answer engines. research found adding quotations (+41%), statistics (+33–40%), and cited sources (+30%) lifts AI visibility, while keyword stuffing hurts it (−9%). Things widely sold as AI-specific levers — llms.txtllms.txt is a proposed (not adopted) Markdown file at /llms.txt that gives AI systems a curated map of a site's most important pages. Proposed by Jeremy Howard in 2024, it's read mostly by coding agents like Claude Code — not search crawlers — and Google ignores it. files and bolting schema onto already-cited pages — show no citation uplift in controlled studies.

Related: GEO, AI Search, Schema Markup for AI, Entity SEO

Changelog