Google AI Search Optimization

Overview

Source Handling

references/google-ai-search-principles.md

Workflow

1. Classify the request:
   • Explain: summarize how Google's generative AI Search features work.
   • Audit: evaluate a site, page, content plan, or SEO recommendation.
   • Plan: create a roadmap, content brief, technical backlog, or prioritized checklist.
   • Implement: modify site code, metadata, structured data, media markup, crawl controls, or content.
   • Myth-check: evaluate AEO/GEO claims, llms.txt, schema claims, AI rewriting, mentions, or chunking advice.
2. Gather only the needed context:
   • For a site audit, inspect crawlability, rendered content, titles, headings, internal links, canonicalization, robots/noindex, sitemaps, page experience, media, structured data, and Search Console constraints if available.
   • For a content task, identify audience, purpose, firsthand expertise, unique value, trust signals, media opportunities, and whether the content would satisfy the visitor without another search.
   • For ecommerce or local tasks, inspect Merchant Center/product data, Product structured data, Business Profile/business details, support details, shipping/returns, reviews, and inventory or offer freshness where relevant.
   • For JavaScript sites, check whether important text, links, media, and structured data appear in rendered HTML and each meaningful screen has a URL.
3. Apply the core recommendations:
   • Build crawlable, indexable, snippet-eligible pages that return successful HTTP status codes.
   • Create non-commodity, helpful, reliable, people-first content with unique experience or expertise.
   • Organize content for humans with descriptive titles, headings, links, URLs, and page structure.
   • Use semantic HTML where practical for readability, accessibility, and navigation; do not overstate perfect HTML validity as a requirement.
   • Add high-quality images and videos when they genuinely help the user, and optimize those assets for discovery.
   • Use structured data for eligible rich results and clearer content meaning, but not as a special AI ranking hack.
   • Keep product, business, local, and support information accurate in Google-facing systems.
   • Improve page experience across mobile, security, speed, layout stability, intrusive elements, and main-content clarity.
   • Monitor with Search Console and test with URL Inspection, Rich Results Test, and PageSpeed Insights when relevant.
4. Explicitly reject unsupported shortcuts:
   • Do not recommend

     llms.txt

     or special AI text files as required for Google AI Search visibility.
   • Do not require content "chunking" into tiny pages or fragments for AI systems.
   • Do not rewrite pages only to target AI systems, long-tail query variants, or query fan-out guesses.
   • Do not create many near-duplicate pages for every possible fan-out query.
   • Do not seek fake mentions, manufactured citations, or reputation signals.
   • Do not overfocus on schema.org markup as if there were AI-specific schema.
   • Do not claim that meeting requirements guarantees crawling, indexing, ranking, or inclusion in AI features.
   • Do not imply every page needs overt SEO work to succeed; prioritize the changes that improve user value, discovery, indexing, and understanding.
5. Produce output that is useful in the user's context:
   • For audits, lead with prioritized findings, then fixes, rationale, and validation steps.
   • For strategy, split work into content, technical, media, ecommerce/local, and measurement tracks.
   • For content briefs, require original expertise, evidence, audience value, media support, and trust signals.
   • For code changes, make narrow edits that improve crawlability, metadata, structured data, accessibility, or page experience, then validate.

References

references/google-ai-search-principles.md

references/audit-playbook.md