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,' 'optimize for Claude/Gemini,' 'llms.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' or 'agent-readable site.' 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.
Check for product marketing context first:
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.
Gather this context (ask if not provided):
| Platform | How It Works | Source Selection |
|---|---|---|
| Google AI Overviews | Summarizes top-ranking pages | Strong correlation with traditional rankings |
| ChatGPT (with search) | Searches web, cites sources | Draws from wider range, not just top-ranked |
| Perplexity | Always cites sources with links | Favors authoritative, recent, well-structured content |
| Gemini | Google's AI assistant | Pulls from Google index + Knowledge Graph |
| Copilot | Bing-powered AI search | Bing index + authoritative sources |
| Claude | Brave Search (when enabled) | Training data + Brave search results |
For a deep dive on how each platform selects sources and what to optimize per platform, see 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:
This is important to read once before doing anything else.
Google's position (AI features 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:
Other AI engines (ChatGPT, Claude, Perplexity, Copilot) behave differently:
Ajouter à Chromellms.txt, structured pricing pages, and machine-readable files when presentWhat this means for the work:
When in doubt, default to "write for people, organize for clarity" — that satisfies both camps.
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:
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.
Before optimizing, assess your current AI search presence.
Test 10-20 of your most important queries across platforms:
| Query | Google AI Overview | ChatGPT | Perplexity | You Cited? | Competitors Cited? |
|---|---|---|---|---|---|
| [query 1] | Yes/No | Yes/No | Yes/No | Yes/No | [who] |
| [query 2] | Yes/No | Yes/No | Yes/No | Yes/No | [who] |
Query types to test:
When your competitors get cited and you don't, examine:
For each priority page, verify:
| Check | Pass/Fail |
|---|---|
| Clear definition in first paragraph? | |
| Self-contained answer blocks (work without surrounding context)? | |
| Statistics with sources cited? | |
| Comparison tables for "[X] vs [Y]" queries? | |
| FAQ section with natural-language questions? | |
| Schema markup (FAQ, HowTo, Article, Product)? | |
| Expert attribution (author name, credentials)? | |
| Recently updated (within 6 months)? | |
| Heading structure matches query patterns? | |
| AI bots allowed in robots.txt? |
Verify your robots.txt allows AI crawlers. Each AI platform has its own bot, and blocking it means that platform can't cite you:
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.
See references/platform-ranking-factors.md for the full robots.txt configuration.
1. Structure (make it extractable)
2. Authority (make it citable)
3. Presence (be where AI looks)AI systems extract passages, not pages. Every key claim should work as a standalone statement.
Content block patterns:
For detailed templates for each block type, see references/content-patterns.md.
Structural rules:
AI systems prefer sources they can trust. Build citation-worthiness.
The Princeton GEO research (KDD 2024, studied across Perplexity.ai) ranked 9 optimization methods:
| Method | Visibility Boost | How to Apply |
|---|---|---|
| Cite sources | +40% | Add authoritative references with links |
| Add statistics | +37% | Include specific numbers with sources |
| Add quotations | +30% | Expert quotes with name and title |
| Authoritative tone | +25% | Write with demonstrated expertise |
| Improve clarity | +20% | Simplify complex concepts |
| Technical terms | +18% | Use domain-specific terminology |
| Unique vocabulary | +15% | Increase word diversity |
| Fluency optimization | +15-30% | Improve readability and flow |
| -10% | Actively hurts AI visibility |
Best combination: Fluency + Statistics = maximum boost. Low-ranking sites benefit even more — up to 115% visibility increase with citations.
Statistics and data (+37-40% citation boost)
Expert attribution (+25-30% citation boost)
Freshness signals
E-E-A-T alignment
AI systems don't just cite your website — they cite where you appear.
Third-party sources matter more than your own site:
Actions:
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
# Pricing — [Your Product Name]
## Free
- Price: $0/month
- Limits: 100 emails/month, 1 user
- Features: Basic templates, API access
## Pro
- Price: $29/month (billed annually) | $35/month (billed monthly)
- Limits: 10,000 emails/month, 5 users
- Features: Custom domains, analytics, priority support
## Enterprise
- Price: Custom — contact sales@example.com
- Limits: Unlimited emails, unlimited users
- Features: SSO, SLA, dedicated account managerWhy this matters now:
robots.txt (for crawlers), llms.txt (for AI context), and AGENTS.md (for agent capabilities)Best practices:
/llms.txt — Context file for AI systems (see 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).
/okf/ — Open Knowledge Format bundle (Google-backed, v0.1)
Google introduced OKF in June 2026 — a markdown spec for representing site content as a directory of cross-linked files with YAML frontmatter, agent-readable without scraping. Built primarily for data-team catalog metadata; the site-readable-by-agents repurposing was popularized by Suganthan Mohanadasan. No confirmed AI-search ranking signal today — treat it as protocol-layer registration like early schema.org. For the full breakdown, implementation paths (free generator, WordPress plugin, by-hand), hosting guidance, and when to skip, see references/okf.md.
Structured data helps AI systems understand your content. Key schemas:
| Content Type | Schema | Why It Helps |
|---|---|---|
| Articles/Blog posts | Article, BlogPosting |
Author, date, topic identification |
| How-to content | HowTo |
Step extraction for process queries |
| FAQs | FAQPage |
Direct Q&A extraction |
| Products | Product |
Pricing, features, reviews |
| Comparisons | ItemList |
Structured comparison data |
| Reviews | Review, AggregateRating |
Trust signals |
| Organization | Organization |
Entity recognition |
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.
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:
What to do:
<main>, <nav>, <article>, <button>, proper heading hierarchy, alt text on images/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:
Not all content is equally citable. Prioritize these formats:
| Content Type | Citation Share | Why AI Cites It |
|---|---|---|
| Comparison articles | ~33% | Structured, balanced, high-intent |
| Definitive guides | ~15% | Comprehensive, authoritative |
| Original research/data | ~12% | Unique, citable statistics |
| Best-of/listicles | ~10% | Clear structure, entity-rich |
| Product pages | ~10% | Specific details AI can extract |
| How-to guides | ~8% | Step-by-step structure |
| Opinion/analysis | ~10% | Expert perspective, quotable |
Underperformers for AI citation:
Citation ≠ recommendation. Getting cited means your content was useful to consult; getting recommended — onto the buyer's actual shortlist — is governed by web-wide consensus (reviews, forums, analysts, press) and is largely independent of your own content. Self-promotional "best [category]" listicles can even backfire for emerging brands: in one 100-query B2B study, 69% of the AI Overview citations that self-promotional listicles earned came in answers that recommended competitors instead of the publishing brand. See references/citations-vs-recommendations.md for the visibility ladder (retrieved → cited → mentioned → recommended), stage-dependent buyer's-guide strategy, what earns recommendations, and the attribution blind spot.
| Metric | What It Measures | How to Check |
|---|---|---|
| AI Overview presence | Do AI Overviews appear for your queries? | Manual check or Semrush/Ahrefs |
| Brand citation rate | How often you're cited in AI answers | AI visibility tools (see below) |
| Share of AI voice | Your citations vs. competitors | Peec AI, Otterly, ZipTie |
| Citation sentiment | How AI describes your brand | Manual review + monitoring tools |
| Recommendation rate | Whether you're on the shortlist, not just cited (see citations-vs-recommendations.md) | Prompt tracking + mention framing |
| Source attribution | Which of your pages get cited | Track referral traffic from AI sources |
| Tool | Coverage | Best For |
|---|---|---|
| Otterly AI | ChatGPT, Perplexity, Google AI Overviews | Share of AI voice tracking |
| Peec AI | ChatGPT, Gemini, Perplexity, Claude, Copilot+ | Multi-platform monitoring at scale |
| ZipTie | Google AI Overviews, ChatGPT, Perplexity | Brand mention + sentiment tracking |
| LLMrefs | ChatGPT, Perplexity, AI Overviews, Gemini | SEO keyword → AI visibility mapping |
Monthly manual check:
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.
Google's guide calls these out explicitly — they hurt across both traditional Search and 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.
/pricing.md fileFor implementation, see the tools registry.
| Tool | Use For |
|---|---|
semrush |
AI Overview tracking, keyword research, content gap analysis |
ahrefs |
Backlink analysis, content explorer, AI Overview data |
gsc |
Search Console performance data, query tracking |
ga4 |
Referral traffic from AI sources |