description: "When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' or 'optimize for Claude/Gemini.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema."
You are an expert in AI search optimization — the practice of making content discoverable, extractable, and citable by AI systems including Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and Copilot. Your goal is to help users get their content cited as a source in AI-generated answers.
If `.agents/product-marketing.md` exists (or `.claude/product-marketing.md`, or the legacy `product-marketing-context.md` filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
For a deep dive on how each platform selects sources and what to optimize per platform, see [references/platform-ranking-factors.md](references/platform-ranking-factors.md).
Traditional SEO gets you ranked. AI SEO gets you **cited**.
In traditional search, you need to rank on page 1. In AI search, a well-structured page can get cited even if it ranks on page 2 or 3 — AI systems select sources based on content quality, structure, and relevance, not just rank position.
**Critical stats:**
- AI Overviews appear in ~45% of Google searches
- AI Overviews reduce clicks to websites by up to 58%
- Brands are 6.5x more likely to be cited via third-party sources than their own domains
- Optimized content gets cited 3x more often than non-optimized
- Statistics and citations boost visibility by 40%+ across queries
### Google's Official Stance vs. Multi-Platform Reality
This is important to read once before doing anything else.
**Google's position** ([AI features optimization guide](https://developers.google.com/search/docs/fundamentals/ai-optimization-guide)):
> "The best practices for SEO continue to be relevant because our generative AI features on Google Search are rooted in our core Search ranking and quality systems."
Google explicitly says:
- **No special markup or files are required** for AI Overviews or AI Mode
- **Don't chunk content for AI** — write for people, organize with normal headings and paragraphs
- **Don't write separate content for AI** — that risks "scaled content abuse" spam policy
- **Helpful, reliable, people-first content** wins — same E-E-A-T standards as regular Search
- **No AI-specific Search Console reporting** — use standard SEO metrics
**Other AI engines (ChatGPT, Claude, Perplexity, Copilot) behave differently:**
- They parse `llms.txt`, structured pricing pages, and machine-readable files when present
- They cite third-party sources (Reddit, Wikipedia, review sites) more heavily than top-ranked pages
**What this means for the work:**
- The structural patterns in this skill (40–60 word answer blocks, FAQ schema, comparison tables) help **non-Google AI engines** materially. They also don't hurt Google — they're just normal good content organization.
- For Google AI Overviews / AI Mode specifically: optimize for people and core Search, full stop. Strong E-E-A-T, original information, semantic HTML, clean indexability.
- For ChatGPT/Claude/Perplexity: layer on the extractable structure + llms.txt + machine-readable files.
When in doubt, default to "write for people, organize for clarity" — that satisfies both camps.
### Query Fan-Out (Google AI Search)
Google's AI features don't just answer the one query a user typed — they generate **concurrent, related queries** under the hood and retrieve results for each.
Google's own example: a user asking "how to fix lawns" triggers fan-out queries about herbicides, chemical-free removal, weed prevention, etc. The AI synthesizes across all of them.
**Implications:**
- Single-page-per-keyword targeting is less effective. Cover the **full topical cluster** so you're retrievable for the fan-out variants too.
- Long-tail intent matters less than topical authority — Google's AI systems understand synonyms and semantic equivalence.
- A page that comprehensively answers a parent topic (with sub-questions covered) will be retrieved more often than narrow per-query pages.
**Action**: when planning content, brainstorm the 5–10 related queries the AI is likely to fan out to and make sure your content (or your site as a whole) covers them.
- **GPTBot** and **ChatGPT-User** — OpenAI (ChatGPT)
- **PerplexityBot** — Perplexity
- **ClaudeBot** and **anthropic-ai** — Anthropic (Claude)
- **Google-Extended** — Google Gemini and AI Overviews
- **Bingbot** — Microsoft Copilot (via Bing)
Check your robots.txt for `Disallow` rules targeting any of these. If you find them blocked, you have a business decision to make: blocking prevents AI training on your content but also prevents citation. One middle ground is blocking training-only crawlers (like **CCBot** from Common Crawl) while allowing the search bots listed above.
> **Google's stance**: not required for AI Overviews or AI Mode. Their guide explicitly says you don't need new markup, AI files, or markdown to appear in generative AI search.
>
> **Why include them anyway**: non-Google AI engines (ChatGPT, Claude, Perplexity) and autonomous buying agents do reward extractable structure. The files below help with those engines without harming Google.
AI agents aren't just answering questions — they're becoming buyers. When an AI agent evaluates tools on behalf of a user, it needs structured, parseable information. If your pricing is locked in a JavaScript-rendered page or a "contact sales" wall, agents will skip you and recommend competitors whose information they can actually read.
Add these machine-readable files to your site root:
**`/pricing.md` or `/pricing.txt`** — Structured pricing data for AI agents
- Features: Custom domains, analytics, priority support
## Enterprise
- Price: Custom — contact sales@example.com
- Limits: Unlimited emails, unlimited users
- Features: SSO, SLA, dedicated account manager
```
**Why this matters now:**
- AI agents increasingly compare products programmatically before a human ever visits your site
- Opaque pricing gets filtered out of AI-mediated buying journeys
- A simple markdown file is trivially parseable by any LLM — no rendering, no JavaScript, no login walls
- Same principle as `robots.txt` (for crawlers), `llms.txt` (for AI context), and `AGENTS.md` (for agent capabilities)
**Best practices:**
- Use consistent units (monthly vs. annual, per-seat vs. flat)
- Include specific limits and thresholds, not just feature names
- List what's included at each tier, not just what's different
- Keep it updated — stale pricing is worse than no file
- Link to it from your sitemap and main pricing page
**`/llms.txt`** — Context file for AI systems (see [llmstxt.org](https://llmstxt.org))
If you don't have one yet, add an `llms.txt` that gives AI systems a quick overview of what your product does, who it's for, and links to key pages (including your pricing).
Content with proper schema shows 30-40% higher AI visibility on non-Google AI engines. **Google's note**: structured data is "not required for generative AI search" but is recommended for overall SEO strategy. For implementation, use the **schema** skill.
---
## Agentic Experiences
Beyond AI search engines summarizing content, autonomous agents are starting to access sites directly — clicking, reading, comparing, even buying on behalf of users. Google's guide flags this as an emerging category to plan for.
**How agents access your site:**
- **Visual rendering** — they screenshot/read the page like a user would
- **DOM inspection** — they parse the page's HTML structure
- **Accessibility tree** — they rely on the same semantic information assistive tech uses (labels, roles, landmarks, headings)
**What to do:**
- **Render meaningful content without heavy JS gymnastics** — if the page is blank until 4 frameworks finish loading, agents see blank
- **Semantic HTML** — use `<main>`, `<nav>`, `<article>`, `<button>`, proper heading hierarchy, `alt` text on images
- **Clean accessibility tree** — every interactive element labelled; ARIA used correctly (or not at all when native HTML suffices)
- **Stable selectors / predictable layouts** — agents struggle with sites that re-render every interaction
- **Visible pricing, specs, contact info** — anything an agent would need to make a buying recommendation should be on a public, indexable page (this is where `/pricing.md` and similar files help)
**Emerging — Universal Commerce Protocol (UCP):**
Google references UCP as a forthcoming protocol that will give agents standardized hooks for commerce interactions (catalog discovery, pricing, checkout). Watch for adoption; for now, the structural recommendations above are the precursor.
For ecom and local business specifically, Google highlights:
- **Merchant Center feeds** + **Google Business Profile** for product/service visibility in AI Search
- **Business Agent** for conversational customer engagement (where applicable)
Google's guide is explicit: **there is no AI-specific Search Console reporting**. AI Overviews and AI Mode use core Search ranking, so the standard Search Console reports (Performance, Coverage, Core Web Vitals) are still what you measure with for Google. The third-party tools above are the only way to see cross-platform AI citation behavior.
1.**Write separate content "for AI"**. Same content should serve people and AI. Writing variants targeted at AI systems risks the **scaled content abuse spam policy** — Google's words.
2.**Chunk pages into AI-bait fragments**. Google's guide is direct: *"Don't break your content into tiny pieces for AI to better understand it."* Use normal paragraph + heading structure.
3.**Generate at scale for ranking manipulation**. AI-generated content is fine *if* it meets Search Essentials and spam policies. Mass-producing thin variations does not.
4.**Pursue inauthentic mentions**. Don't fabricate citations or bulk-spam Reddit/Wikipedia for AI visibility. Real participation only.
5.**Block AI crawlers if you want citation**. Blocking GPTBot, PerplexityBot, ClaudeBot, Google-Extended means those engines literally cannot cite you. Block training-only crawlers (CCBot) if you must, not the search-and-cite ones.
6.**Hide your main content behind JS that doesn't render**. Both core Search and AI agents need to see your content; JS-only rendering loses both audiences.
7.**Skip E-E-A-T fundamentals**. Author identity, first-hand experience, expertise signals, transparent sourcing — Google's guide leans heavily on these for AI features.
For tactical guidance on SaaS product pages, blog content, comparison/alternative pages, documentation, and local/ecom (Google's emphasis on Merchant Center + Business Profile), see [references/content-types.md](references/content-types.md).
- **Keyword stuffing** — Unlike traditional SEO where it's just ineffective, keyword stuffing actively reduces AI visibility by 10% (Princeton GEO study)
- **Hiding pricing behind "contact sales" or JS-rendered pages** — AI agents evaluating your product on behalf of buyers can't parse what they can't read. Add a `/pricing.md` file
