AI Search Optimization (GEO)
Get your brand into the answer when people ask ChatGPT, Perplexity, Google AI, Gemini, or Copilot
a question in your space — instead of watching a competitor get named. This is GEO (Generative
Engine Optimization; also AEO/LLMO): optimizing to be cited and recommended by AI engines. It
supplements search/SEO; it doesn't replace it.
Four truths shape everything:
- AI answers are built by retrieval + fan-out. Engines retrieve live from search indexes
(ChatGPT via OpenAI's own crawler/index, OAI-SearchBot — historically Bing-seeded; Google feeds
AI Overviews/AI Mode) and split your topic into sub-queries — so ranking in search feeds AI
citation, and you optimize for a constellation of questions.
- AI cites community/social sources most. Reddit, YouTube, Wikipedia, LinkedIn, listicles and
review sites dominate citations — the cited pages are usually not your pages. Earned mentions
beat product pages.
- Platforms disagree. ChatGPT skews Wikipedia, Perplexity skews Reddit, AI Overviews lean on
E-E-A-T + the community web. Optimizing for one ≠ all.
- Extractable, fresh, well-sourced content gets quoted. Quotations, statistics, citations, Q&A
structure, and schema lift citation; stale content gets displaced.
(Full mechanics:
references/how-ai-engines-cite.md
.)
Step 0 — Read the foundation + the goal
Load
and
(entity clarity + the real questions matter). Identify the
queries the user wants to be recommended for and the
engines their audience uses.
Step 1 — Run the prompt-audit (always start here)
Ask the user's
10–30 buyer-intent queries (plus fan-out sub-questions) across ChatGPT / Perplexity
/ Gemini in fresh sessions; document
whether the brand appears, how it's described, and which sources
are cited. The cited sources
are the strategy; the gaps are the content list. This is the honest
ground-truth method — see
references/audit-and-measurement.md
.
Step 2 — Be retrievable (the foundation)
If you can't be found in search, you can't be cited: rank in
Google/Bing and in platform search →
(the sibling). Same keyword/question research powers both. And
verify AI retrieval
crawlers can reach the site — robots.txt and CDN/bot-protection defaults (e.g. Cloudflare) often
block OAI-SearchBot / ChatGPT-User / PerplexityBot / Claude's bots unintentionally.
Step 3 — Earn brand mentions across cited sources (the social core)
Where AI looks most — done
authentically: valuable
Reddit participation in buyer-intent
threads;
YouTube with brand + keywords in
titles/transcripts (a top AI-Overview signal);
LinkedIn expertise;
Quora; earned
"best [X]" listicle and
review-site (G2/Trustpilot)
inclusion; relationship-driven PR. The goal is a
web of mutual verification. See
references/the-geo-levers.md
.
Step 4 — Make content extractable
So a model can lift a clean claim: lead with a
TL;DR answer,
question-shaped headings, lists/
tables,
quotations + verifiable stats + citations (the research-backed levers),
FAQ/Article
schema,
named author + dates, and keep it
fresh (citations decay). (This lever spans your
website/blog too — broader than social; pair with
.)
Step 5 — Build entity clarity
Give the model a clean entity to recommend: a
consistent one-line description across site/profiles/
listings →
; Wikipedia/Wikidata
if genuinely notable; claimed listings + consistent
NAP; a corroborated "the X for Y" position.
Step 6 — Measure (honestly) + the boundary
Re-run the
audit monthly (expect a multi-week lag; judge over quarters), optionally add a GEO
tracking tool, and watch
AI referral traffic (chatgpt/perplexity referrers).
Never fabricate a
"share of voice" or citation %. No WoopSocial analytics. Sibling boundary:
=
found in platform + Google search;
this = cited by AI answer engines.
Orchestration map
ai-search-optimization sets the AI-visibility layer; it routes to / pairs with:
(retrieval/search foundation — sibling) ·
(entity) ·
(question
clusters) ·
/ the growth skills (the YouTube/Reddit/LinkedIn content that earns
mentions) ·
viral-reverse-engineering
(what gets cited/shared) ·
(publish).
Quality bar — self-check
- Did I start with the prompt-audit and let the cited sources drive strategy?
- Did I apply the four levers (retrievable → earned mentions → extractable → entity), foregrounding
the community/social plays?
- Did I respect that AI cites earned/community sources over product pages, and that platforms
differ?
- Did I keep it authentic (refuse astroturfing/fake reviews) and never fabricate share-of-voice
numbers?
- Did I hand the search/retrieval foundation to , note GEO spans the web too, and
use audit/tools/referral measurement (no WoopSocial analytics)?
Edge cases & pushback
- "Flood Reddit / buy reviews" → refuse astroturfing; it's detectable, removed, and trust-destroying
→ authentic participation + earned reviews.
- "Tell me my AI share of voice %" → can't see inside models; run the audit / a tool; don't invent.
- "Just optimize my product page" → that's ~3% of it; most citations are earned/community sources.
- "Optimize for AI search" (one thing) → engines differ (ChatGPT≠Perplexity≠AI Overviews); pick the
field.
- "Is this my TikTok/Google SEO?" → related but distinct → owns platform/Google search.
- "Does WoopSocial track this?" → no; measure via audit + GEO tools + referral analytics.
- AI-generated content dump → AI down-weights low-quality AI content; needs human judgment + sources.
Related skills
- — the sibling: platform + Google search (the retrieval foundation AI pulls from).
- — the entity/positioning AI must understand; — question clusters.
- — the how of credible Reddit participation (the top AI-citation source).
- , // — the YouTube/Reddit/LinkedIn
content that earns the mentions AI cites.
viral-reverse-engineering
References
references/how-ai-engines-cite.md
references/the-geo-levers.md
references/audit-and-measurement.md
- — a worked audit + Reddit/YouTube/extractability/entity plays + honest scope.