Generative Engine Optimization: What Google Documents vs What Agents Still Do

Human-Edited & Verified • Deterministic AI-assisted research. Editorial standards.

AI Edited

Last updated: 26 August 2026

Generative engine optimization is the practice of earning citations and mentions inside AI answers. On Google Search that practice is still SEO: unique pages in the index, crawlable HTML, measured in Search Console—not a separate ranking algorithm.

TLDR

• Google’s AI optimization guide states generative features use RAG on the Search index and query fan-out. Foundational SEO still applies.
• The same guide says you can ignore llms.txt, “chunking,” and inauthentic mentions for Google Search.
• Measure Google with the Generative AI performance report.
• seo-geo still earns its keep as a writing and multi-engine skill: extractable passages, entity grounding, ChatGPT/Perplexity crawl gates.
• Deep playbook: GEO guide. Hub: Skills directory.

Contents

• What generative engine optimization actually is
• Google's official AI search rules
• RAG and query fan-out
• Run the seo-geo skill
• Measure citations without fake dashboards
• FAQ
• Related reading
• Sources

What generative engine optimization actually is

GEO is a label for “be the page an answer engine quotes.” Google Search Central treats AEO/GEO hacks as mostly unsupported. Unique, people-first content and technical eligibility remain the levers.

This pillar is the product skill. The longer GEO guide is the editorial playbook. Agent silos (/claude/seo-geo, /chatgpt/seo-geo) are install recipes. Do not copy the same H1 onto those URLs.

Google's official AI search rules

AI Edited

From the AI optimization guide:

• Pages must be indexed and snippet-eligible.
• Create non-commodity content with a real point of view.
• Semantic HTML helps humans and assistive tech; Google does not require “perfect” markup.
• Structured data is for rich results, not a secret AI schema.
• Site must remain included via Search Console generative AI controls.

Myth list Google says you can skip for Google Search: special AI text files (including llms.txt), mandatory chunking, rewriting prose “for the model,” chasing fake mentions.

llms.txt still has a job for coding agents. That job lives on the llms.txt generator skill. Do not sell it as an Overviews ranking factor.

RAG and query fan-out

Google describes RAG as retrieving ranked pages, then generating with citations. Query fan-out issues related sub-queries (their herbicide/weeds example). A page can be cited for a sub-question even when it would not rank for the typed query.

Practical writing: one H2, one direct answer, then evidence. That is extractability for readers and for RAG—not a token-length superstition. Keep schema skill for rich-result eligibility, not as GEO magic.

Run the seo-geo skill

AI Edited

Product SKILL.md focuses on passage citability, entity sameAs grounding, and multi-source brand facts:

npx @seoskillsai/cli add seo-geo
npx @seoskillsai/cli run seo-geo --target "https://example.com"

Use the homepage installer for multi-agent wiring. After the skill flags thin commodity sections, rewrite with first-party evidence (benchmarks you actually ran). Run an audit if the page is not indexable.

Measure citations without fake dashboards

Google: Generative AI performance report. Query-level citation/mention logs: AI citation tracker skill. Inclusion toggle: generative AI control.

ChatGPT search is not that report. Allow OAI-SearchBot if you want ChatGPT search citations (OpenAI bots).

FAQ

What is generative engine optimization?

Work that makes your pages eligible to be retrieved and cited inside AI answers. On Google, that work is documented as ordinary SEO plus measurement in Search Console.

Is GEO different from SEO?

Google says generative features are rooted in core ranking and quality systems. Other engines (ChatGPT search, Perplexity) use their own crawlers and indexes.

Do I need llms.txt for Google AI Overviews?

Google says Search does not use llms.txt as a special ranking file. Keep llms.txt for agent discovery if you want it.

How does query fan-out work?

The model issues related sub-queries and retrieves additional results, then stitches an answer with citations.

How do I measure GEO success?

Use GSC generative AI impressions for Google. Add a citation/mention log for a fixed query set. Do not treat vendor “share of voice” as Google’s metric.

Related reading

• GEO editorial guide
• AI citation tracker
• llms.txt generator
• SEO audit

Conclusion

Optimize for Google’s documented systems; use seo-geo for extractable writing and non-Google crawlers. Search generative engine optimization seoskillsai when you need the skill pack again.

Sources

• Google AI optimization guide
• Generative AI performance report
• Search generative AI control
• OpenAI crawlers
• SEO Skills AI GitHub