Google's official AI optimization guide: what actually matters

According to Google, its generative AI features rely on two distinct mechanisms: RAG (retrieval-augmented generation), which fetches relevant web pages to ground a reliable, cited answer, and 'query fan-out', which generates related queries to complement the answer with additional results. Both mechanisms rely on Google Search's historic ranking systems.

The direct consequence: there is no separate discipline to apply to be visible in Google's generative AI. The same fundamentals — indexing, relevance, authority, structure — still apply, at an equivalent bar.

The guide insists on content designed for real readers rather than for an algorithm:

• Bring a genuine point of view or expertise, rather than rephrasing information already available elsewhere.
• Write to be useful and reliable ("people-first"), not to manipulate a ranking.
• Structure content with clear headings, sections and paragraphs.
• Illustrate with relevant images and videos, following the SEO best practices specific to those formats.

The guide restates classic technical requirements, which become entry conditions to be picked up by generative AI systems:

• Be indexable and crawled without friction: a well-managed crawl budget, JavaScript that does not block access to content.
• Use semantic HTML, which makes content easier to understand for automated systems and screen readers alike.
• Reduce duplicate content, which wastes crawl resources and hurts user experience.
• Validate the site in Search Console to quickly diagnose technical issues.

The guide takes care to cut short several misconceptions that have circulated since the rise of GEO/AEO:

• The llms.txt file or any 'special AI' markup: Google Search ignores them, with no impact on ranking or visibility.
• Chopping content into small fragments: Google's systems understand entire pages, and no "ideal" length exists.
• Rewriting content "for AI": the systems already understand synonyms and variants, no specific writing style is required.
• Structured data as a prerequisite for generative AI: it remains useful for classic rich results, but does not gate appearing in AI answers.

To measure visibility, Google recommends its 'AI features performance' report in Search Console. It is a useful tool — and the only officially reliable one for this scope — but it is worth being precise about what it covers: only Google's own AI surfaces (AI Overviews, AI Mode).

It says nothing about what ChatGPT, Claude, Perplexity, or Gemini used as a standalone conversational assistant answer when a prospect asks them "what is the best tool for X" or "an alternative to Y". These uses, outside of Google Search, account for a growing share of research and purchase recommendations.

Applying the fundamentals Google describes — unique content, clean technical structure — remains the right foundation to be cited by any generative system, Google included. But checking whether it actually works, engine by engine, requires observing the answers themselves, beyond one provider's report on its own scope. This is exactly what AIglebot tracks: prompt by prompt and engine by engine (ChatGPT, Gemini, Perplexity, Claude), who is cited, how often, and at what rank — to know whether content and technical-structure efforts pay off beyond Google.