AI Overviews and generative search: what Google officially recommends – and what you can ignore

In May 2026, Google Search Central published “Optimizing your website for generative AI features on Google Search”. The core message: generative features are grounded in the same indexing and quality systems as classic Search. Retrieval-augmented generation (RAG) and query fan-out retrieve relevant indexed pages – they do not invent a parallel web only for AI Overviews.

The practical implication is blunt but useful: optimising “only for AI Overviews” while neglecting crawlability, snippets, helpful content and local signals works against official guidance. SEO remains SEO; terms like AEO/GEO do not, from Google’s perspective, define a separate discipline with secret levers.

Google stresses unique, valuable, people-first content more than micro technical tricks. Commodity content – interchangeable tip lists any chat model can reproduce – helps neither readers nor systems. Non-commodity content brings original experience, transparent method, local or industry evidence and clear ownership.

That aligns with the helpful content approach: write for people, demonstrate expertise, and use SEO best practices as amplifiers – not substitutes for substance. For agencies this means case studies with measurement windows, guides with primary sources and honest limits beat ten thin city pages.

The AI guide explicitly points back to Search Essentials, crawlability, JavaScript SEO basics and page experience. Pages must be indexable and eligible to show with a snippet to be considered for generative features. Google also notes inclusion in generative AI features in Search Console as part of eligibility.

In practice: HTTPS, sensible robots rules, canonicals, hreflang for multilingual sites, solid mobile presentation and no main content trapped behind opaque client rendering. Weakness here is not an “AI SEO gap” – it is a foundation gap.

In the AI guide Google lists practices you need not prioritise for Google Search: special llms.txt files (Search does not use them as a ranking lever), artificial “chunking” only for models, rewriting copy “for AI”, and hunting inauthentic mentions. Structured data remains useful for rich results – yet there is no separate mandatory schema solely for generative AI.

Equally important: scaled content abuse. Large volumes of lightly varied pages created mainly to manipulate rankings or AI answers conflict with spam policies. Quality and uniqueness scale better than page count.

Use this checklist before publishing a guide or service page. It operationalises questions from the helpful content and AI guides – without turning them into a magic score.