learn · SEO / AEO / GEO
The LLMRanks learning center: practical guides to technical SEO, on-page optimization, structured data, content strategy, off-page authority, Core Web Vitals, answer engine optimization, generative engine optimization, and how ChatGPT, Gemini, Claude and Perplexity actually retrieve and cite sources.
02 · the visibility stack
A page that ranks and gets cited by AI engines clears every layer in order: crawlers and indexers need access before meaning matters, entities need to be clear before authority can flow to them, and evidence and consensus decide whether an engine trusts the answer enough to name or link the source. Every pillar below maps onto one of these layers.
03 · pillar grid
Each pillar covers one layer of the stack above. Guides ship as the underlying research is validated and written — everything else is queued and on its way.
Access layer
How crawlability, indexing, sitemaps, redirects and rendering decide whether search engines and AI crawlers can use your site at all.
Meaning layer
Titles, headings, first answers, semantic structure and media text — making a page say its answer in the language users and engines retrieve.
Entity layer
What schema markup actually earns — rich results and entity clarity — and what it does not: schema is not an AI citation lever.
Authority flow
Site architecture, link equity, anchors and topic clusters — routing authority to the pages that should rank and be cited.
Evidence layer
Original evidence, expertise signals, factual density and lifecycle — content that earns trust from readers, Google and AI engines.
Consensus layer
Brand mentions, backlinks, communities, video and branded search — the third-party consensus that shapes rankings and AI recommendations.
Experience layer
LCP, INP, CLS and the performance stack behind them — what page experience really costs you in rankings, and what it doesn't.
Citation layer
How LLMs choose what to cite, what makes content quotable, and how answer/generative engine optimization extends SEO without replacing it.
1 guide
Engine layer
How ChatGPT, Claude, Perplexity, Gemini and Copilot retrieve, rank and cite sources — and why each engine rewards different work.
Future-proofing
What changed, what died and what now matters: algorithm updates, llms.txt reality, AI crawler policy, entities and the zero-click future.
Agent layer
Designing sites AI agents can actually use — how agents read pages, what breaks them, and how to detect agent traffic today.
04 · start here
Begin with AEO & GEO: how large language models pick what to cite, what makes a passage quotable, and how this extends — not replaces — SEO.
Start here
Start with AI Search Engines: how ChatGPT, Claude, Perplexity, Gemini and Copilot each retrieve and cite sources differently from classic Google ranking.
Start here
06 · evidence log
The full studies live on their own pages. This trail is the short version: what changed, and when.
A competitor analysis found a glossary-plus-sitewide-linking model ranking many long-tail SEO terms, but also exposed a gap in contextual in-body links.
Cross-engine overlap was tiny, Reddit led citations, YouTube concentrated in AI Overviews how-to answers, and named-brand visibility diverged from source linking.
The KB downgraded blanket FAQ-ification, softened schema-as-AI-lever claims, strengthened fan-out coverage, and elevated original research.
A synthesis of recent AI SEO commentary captured market disagreement around prompt tracking, schema, llms.txt, review influence, and off-site mentions.
07 · source library
This list should keep expanding. The rule: public claims get public receipts wherever possible.