Optimizing your website for generative AI features on Google Search.
Google just published official guidance on optimizing for generative AI search. The mythbusting section is worth reading carefully and worth reading critically.
Google says you don't need llms.txt files, content "chunked" for AI parsing, pages rewritten specifically for AI systems, inauthentic mention-seeking, or overfocusing on structured data.
For Google Search's AI features, AI Overviews, and AI Mode, most of that is accurate. Google's RAG-based systems pull from their existing search index. Good SEO, non-commodity content, and clear technical structure already get you there. The GEO industry has created unnecessary complexity around features that are, for Google, just SEO by another name.
But the guidance is narrower than it reads. It applies specifically to Google Search's generative features. It does not apply to direct agent queries operating outside Google's search index. When a consumer's agent queries your product catalog through ACP or UCP to execute a transaction, it is not going through Google's RAG pipeline. It needs machine-readable structured data, metafields, and API-accessible checkout, exactly what Google says not to overfocus on.
It also doesn't apply to non-Google platforms. Perplexity, ChatGPT, and Claude each have their own retrieval architectures. The llms.txt guidance is Google-specific.
Google even points to this gap itself: "Protocols like Universal Commerce Protocol are emerging that will allow Search agents to do more." That sentence covers the layer that their own guide doesn't address.
The takeaway: for Google Search AI features, good SEO is sufficient. For direct agent commerce outside the Google index, structured data and protocol integration remain critical. Read Google's guide before following any GEO advice that doesn't specify which platform it's optimizing for.
