Ai Search Optimization

Use to get a brand and its content CITED and RECOMMENDED by AI answer engines — the GEO (Generative Engine Optimization) / AI-search-visibility skill. Run when the user says "GEO," "get cited by ChatGPT/Perplexity/Google AI," "ChatGPT SEO," "LLM SEO / LLMO," "AI Overviews," "answer engine optimization (AEO)," "will AI recommend my brand," or wants to show up in AI-generated answers, not just the feed or Google links. Reads brand-profile and audience first. AI engines retrieve + fan-out and cite community/social sources heavily (Reddit, YouTube, Wikipedia); platforms disagree; earned media beats product pages; extractable, fresh content drives citation. Covers the four GEO levers (retrievable, earned mentions, extractable content, entity clarity), authentic social plays, and the manual prompt-audit method. Refuses astroturfing; never fabricates "share of voice." Sibling of social-seo (platform + Google search). Judges via the audit + GEO tools + AI referral traffic.

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:

1. 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.
2. 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.
3. Platforms disagree. ChatGPT skews Wikipedia, Perplexity skews Reddit, AI Overviews lean on E-E-A-T + the community web. Optimizing for one ≠ all.
4. 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 brand-profile.md and audience.md (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 → social-seo (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 social-seo.)

Step 5 — Build entity clarity

Give the model a clean entity to recommend: a consistent one-line description across site/profiles/ listings → brand-profile; 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: social-seo = 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: social-seo (retrieval/search foundation — sibling) · brand-profile (entity) · content-pillars (question clusters) · reels-script / the growth skills (the YouTube/Reddit/LinkedIn content that earns mentions) · viral-reverse-engineering (what gets cited/shared) · scheduling-and-queue (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 social-seo, 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 → social-seo 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

• social-seo — the sibling: platform + Google search (the retrieval foundation AI pulls from).
• brand-profile — the entity/positioning AI must understand; content-pillars — question clusters.
• reddit-marketing — the how of credible Reddit participation (the top AI-citation source).
• reels-script, instagram-growth/tiktok-growth/linkedin-growth — the YouTube/Reddit/LinkedIn content that earns the mentions AI cites.
• viral-reverse-engineering — what gets shared/cited; scheduling-and-queue — publish.

References

• references/how-ai-engines-cite.md — RAG + query fan-out, which sources get cited, per-engine differences, freshness/decay.
• references/the-geo-levers.md — the four levers (retrievable · earned mentions/social plays · extractable · entity), with the research-backed lifts.
• references/audit-and-measurement.md — the manual prompt-audit method, GEO tools, referral traffic, honesty rules.
• references/examples.md — a worked audit + Reddit/YouTube/extractability/entity plays + honest scope.