GEO and AEO: staying visible in the age of AI search

Ask a question today and there is a good chance you never see a list of ten blue links. You ask ChatGPT, Perplexity, Gemini, or Google's AI Overviews, and a single synthesized answer comes back — already written, already sourced, already deciding which companies get mentioned and which do not. If your content is not the thing being quoted in that answer, you are effectively invisible, even when you still rank number one on the old-fashioned results page. Two disciplines have grown up to address exactly this shift: Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO).

What AEO and GEO are

AEO — Answer Engine Optimization is about being the answer rather than a link to it. It grew out of featured snippets, voice assistants, and "position zero," and it now extends to AI answer engines. Its foundation is structured data and direct, self-contained question-and-answer content that a machine can lift verbatim.

GEO — Generative Engine Optimization is newer and narrower: it is the practice of being cited, quoted, or represented inside a generative AI answer — the paragraph ChatGPT writes, the summary Perplexity assembles, the box Google AI Overviews shows above the results. Where AEO asks "can a machine extract my answer?", GEO asks "when a model composes a fresh answer from many sources, does mine make the cut and get credited?"

Neither replaces classic SEO — they build on it. A model still has to crawl, index, and understand your page before it can quote it, so the fundamentals — crawlability, clean structure, genuine authority — still matter. AEO and GEO simply add a new goal on top of ranking: being the source the machine chooses to speak with.

flowchart TB
  subgraph SEOflow["Classic search · SEO"]
    direction LR
    Q1["Query"] --> L["List of links"]
    L --> CL["Click a link"]
    CL --> P["Your site"]
  end
  subgraph AIflow["AI answer · AEO / GEO"]
    direction LR
    Q2["Query"] --> ENG["Engine reads<br/>many sources"]
    ENG --> ANS["One ready-made answer<br/>with source links"]
    ANS --> CIT{"Is your content<br/>cited?"}
    CIT -->|yes| VIS["You are in the answer"]
    CIT -->|no| INV["You are invisible"]
  end

How and why this emerged

None of this existed as a named discipline a few years ago. The shift tracks a short, dense timeline. The public launch of ChatGPT in November 2022 made conversational, synthesized answers a mainstream habit almost overnight. Google followed with its Search Generative Experience, which matured into AI Overviews now shown above ordinary results for a large share of queries. Perplexity built its whole identity around the phrase "answer engine," returning a written answer with inline citations instead of a page of links. The common thread is the rise of zero-click search: the answer is synthesized on the results surface, and the user never visits the page it came from.

flowchart LR
  A["November 2022<br/>ChatGPT launches"] --> B["2023<br/>Google SGE —<br/>generative search"]
  B --> C["November 2023<br/>The «GEO»<br/>research paper"]
  C --> D["2024<br/>Google AI Overviews<br/>above the results"]
  D --> E["Now<br/>Perplexity and other<br/>«answer engines»"]

The term GEO itself comes from research. A 2023 academic paper titled GEO: Generative Engine Optimization — from researchers associated with Princeton, Georgia Tech, the Allen Institute for AI, and IIT Delhi, later presented at KDD 2024 — was the first to frame the problem formally and to measure which content changes actually increase how often a source is cited inside a generated answer. The details are worth reading, but the headline shift is the one that matters for practitioners: the goal moved from ranking a page so a human clicks it, to being quoted in a generated answer the human may never click at all.

How GEO and AEO differ from classic SEO

Classic SEO optimizes a page to rank — to appear high in a list so a person clicks through. AEO and GEO optimize to be the extracted or cited answer — the text a machine repeats or the source it credits. The end state is different, so the signals that matter shift with it.

Structure over keywords. A ranking page can bury its answer three scrolls down; an answer engine needs the answer stated plainly, early, and in machine-readable form.

Quotable facts over persuasion. Models lift clear, self-contained statements. A sentence that stands on its own — a definition, a number, a direct yes or no — is far more likely to be quoted than a paragraph that only makes sense in context.

Freshness and clarity over volume. Current, unambiguous, well-sourced facts win citations; walls of vague prose do not.

Authority still decides. E-E-A-T — experience, expertise, authoritativeness, trust — matters as much as ever, because a model, like a careful editor, prefers to quote a source it has reason to trust.

Crawler access is now a first-class concern. A generative engine can only cite what its crawler was allowed to read. If your robots.txt blocks AI bots, you have opted out of the answer entirely.

How we apply it at MiCode

We do not treat this as a checkbox on one page; we run it as a portfolio-wide practice across every site we own — this landing page and our product sites — and the pattern is consistent enough to describe as a recipe.

flowchart LR
  SITE["Our sites<br/>mi-code.pl · eksiegowyai.pl · ai-budget.pl"] --> LLMS["llms.txt<br/>+ llms-full.txt"]
  SITE --> ROB["robots.txt<br/>welcomes AI bots,<br/>fences off private routes"]
  SITE --> JLD["JSON-LD<br/>FAQPage · Organization"]
  SITE --> PRE["Trilingual prerender<br/>canonical + hreflang"]
  LLMS --> CRAWL["AI crawler<br/>reads the site"]
  ROB --> CRAWL
  JLD --> CRAWL
  PRE --> CRAWL
  CRAWL --> ANS["AI answer<br/>cites and mentions you"]

On mi-code.pl we publish a curated, structured llms.txt — a plain-text map of the company, products, blog, and FAQ, written for language models to read directly. Our robots.txt deliberately welcomes AI crawlers — GPTBot, ChatGPT-User, ClaudeBot, anthropic-ai, PerplexityBot, Google-Extended, CCBot — rather than blocking them, because a crawler we lock out is an answer we can never appear in. Every article emits FAQPage structured data (JSON-LD), baked per-locale into the static prerender, alongside Organization schema. The whole site is prerendered into three real, crawlable locale trees — /, /en/, and /ru/ — each with a self-referencing canonical and reciprocal hreflang (with x-default pointing to Polish), so an engine reading in any of the three languages lands on a clean, indexable URL. The content itself is semantic and scannable: headings, callouts, and direct-answer FAQs, plus a sitemap.

On eksiegowyai.pl, our accounting product, the same recipe adapts to a live app. A structured llms.txt maps the product, guides, articles, and company; the robots.txt welcomes an even broader set of AI crawlers — including GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot, Claude-User, anthropic-ai, PerplexityBot, Perplexity-User, Google-Extended, Applebot-Extended, and CCBot — while explicitly disallowing the private surface: API, admin, dashboard, chat, KSeF, settings, subscription, and auth routes. Public knowledge is open to machines; a user's data is not.

On ai-budget.pl, our budgeting app, we go one step further and publish both a concise llms.txt and an llms-full.txt — a full-markdown mirror of the content for models that want the complete text rather than a map. The robots.txt again welcomes the broad AI-crawler set, and a sitemap ties it together.

The shape is the same everywhere, and it is easy to copy: first, make the site machine-readable with llms.txt and structured data; second, explicitly welcome AI crawlers while fencing off private routes; third, answer real questions directly, with an FAQ and schema behind it.

A practical checklist

If you want your own content to survive the shift, a handful of concrete moves cover most of the ground.

Answer the question directly, early. State the answer in the first paragraph, in a sentence that stands on its own. Everything else can elaborate; the quotable line should come first.

Add a real FAQ with FAQPage schema. Write the questions your audience actually asks, answer each in a self-contained paragraph, and mark them up with FAQPage JSON-LD so a machine can parse them without guessing.

Keep facts quotable and current. Prefer clear statements, real numbers, and dated sources over vague prose. Update them when reality moves; a stale fact is a lost citation.

Publish a structured llms.txt. Give models a clean, plain-text map of what you offer and where it lives. It costs almost nothing and makes your site legible to the systems now doing the answering.

Let AI crawlers in — deliberately. Allow the major AI bots in robots.txt while disallowing private, app, and auth routes. You cannot be cited by an engine you have blocked.

Use semantic HTML and JSON-LD. Real headings, lists, and structured data (Organization, FAQPage) give machines the scaffolding to understand and reuse your content correctly.

Build genuine authority. E-E-A-T is not a trick; publish under real expertise, cite real sources, and earn the trust that makes a model comfortable quoting you.

Thinking about how your product or content shows up when an AI answers on your behalf? We build sites and products that are legible to both people and machines — structured data, llms.txt, a sane crawler policy, and content written to be quoted. Reach out at [email protected] and we will help you get found in the answer, not just on the results page.