AEO & GEO knowledge base

Understand how AI decides what to answer.

Rankus AI is a free knowledge platform on Answer Engine Optimization and Generative Engine Optimization — explainers, a glossary and published benchmarks on how ChatGPT, Perplexity, Gemini, Grok, Meta AI, DeepSeek and AI Overviews choose their sources.

Updated daily

Anatomy of an AI answer

“Which project management tool is best for small agencies?”

1

Retrieval

The model gathers candidate passages, not whole websites.

2

Synthesis

Consensus across sources beats any single claim.

3

Citation

Only a few sources survive into the visible answer.

simplified model of a retrieval-augmented answer

Systems we document
ChatGPTPerplexityGeminiClaude CopilotGrokMeta AIDeepSeekAI Overviews

AEO and GEO, without the buzzwords

The two terms get used interchangeably. They describe different problems. Here is the distinction we use across the site.

AEO

Answer Engine Optimization

A content and structure question: can a machine extract a clean, attributable answer from a page at all?

  • Question-level page architecture
  • Entity clarity and structured data
  • Extractable answer blocks

GEO

Generative Engine Optimization

A sourcing question: which reviews, forums, directories and datasets does the model reach for before it writes?

  • Retrieval corpora and source bias
  • Consensus and third-party mentions
  • Brand facts across the open web
Mechanics of a citation

How AI answers actually get made

Search used to end with a page of ten blue links, each one a bet the reader might click. Increasingly, it ends with an answer someone else already assembled. People ask a chatbot or an AI Overview instead of scanning results themselves, and what comes back arrives pre-written: a short, direct answer built from a handful of sources the model judged good enough to use, with a citation or two attached if the reader is lucky. Most of what it gathered along the way, entire pages of careful writing, never surfaces at all. Three shifts decide who ends up in that handful, and none of them are about ranking higher for a keyword.

Source selection, simplified

How a citation survives Six candidate pages. Three dense, unstructured pages are dropped before the answer is written. Three question-shaped pages, each ending in a clean answer block, converge into the final cited answer. DENSE, UNSTRUCTURED QUESTION-SHAPED THE ANSWER
simplified model of source selection
1

Extraction beats ranking

A page used to compete for a position on a results list. Now it competes to be lifted whole. What matters is whether a model can pull out a clean, self-contained answer, a definition, a step, a comparison, without reconstructing it from scattered paragraphs and sidebars. A page that ranks well but never yields a usable block still gets read, and still gets skipped when the answer is written.

2

Consensus beats a single claim

A model rarely writes from one source alone. It gathers several, checks where they agree, and weights the answer toward whatever holds up across independent pages rather than whichever page argued it best. A claim that exists in only one place, however well written, gets treated as unverified rather than authoritative, no matter how confidently it is stated there.

3

Entities beat keywords

Before a model reasons about a page at all, it resolves the brands, people and products named on it into entities, then checks whether the rest of the web describes those same entities consistently. A page can use exactly the right keywords and still lose, if the facts sitting around it elsewhere disagree with what the model already believes about that entity.

What Rankus AI documents

Rankus AI documents the mechanics behind that shortlist. That means question-shaped page architecture: structuring content around the question a reader, or a model, actually brings to it, rather than around a topic. It means studying what makes an answer block genuinely extractable rather than merely well written. It means tracking entity consistency: how a brand, a product or a person needs to be described across a site, and beyond it, before a model treats those facts as settled rather than disputed. And it means watching how differently ChatGPT, Perplexity, Gemini and other engines cite sources for the same question, since none of them draw from quite the same shortlist or weigh the same signals the same way. Every explainer here traces back to real pages and real answers a model actually produced, not to theory.

The blog is where that documentation lives, case by case, updated as the engines themselves keep changing. Read the blog →

  • Question-shaped architecture
  • Extractable answer blocks
  • Entity consistency
  • Cross-engine citation behavior

Latest posts

Published daily — the eight most recent.

All articles
Core / PillarSeptember 2026 AEO vs SEO: What's the Difference? (2026 Guide) I compare AEO vs SEO side by side: the overlap, the workflow that changes, and how I measure citations next to rankings. 26 min read Core / PillarSeptember 2026 GEO vs SEO: What's the Difference? (2026 Guide) A field side-by-side of geo vs seo: overlap, citation versus ranking, and the workflow I changed for answer engines. 28 min read Core / PillarSeptember 2026 AEO vs GEO: What's the Difference? (2026 Guide) Field notes on aeo vs geo: what each term means in my work, where they overlap, and which label I pick when. 26 min read Core / PillarSeptember 2026 What Is llms.txt? (2026 Guide) Field notes on what is llms.txt: the spec, 2026 adoption, and whether the file moved AI citations in my tests. 24 min read Core / PillarSeptember 2026 What Is an AI Crawler? (2026 Guide) I define what an AI crawler is, name the bots I see, and explain the robots.txt tradeoffs behind citations. 24 min read Core / PillarSeptember 2026 What Is Perplexity AI? (2026 Guide) I break down Perplexity as a real-time answer engine and the AEO moves I use so brand pages get retrieved and cited. 24 min read Core / PillarSeptember 2026 What Is ChatGPT Shopping? (2026 Guide) A practitioner’s 2026 guide to what ChatGPT Shopping is, how products get recommended, and the merchant requirements I actually check. 24 min read Core / PillarSeptember 2026 What Are Google AI Overviews? (2026 Guide) Field notes on how Google AI Overviews work in 2026, when they trigger, and what that does to clicks. 27 min read

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