Google releases its official AI SEO guide: it’s classic SEO

Google’s official guide for AI search is overwhelmingly a reminder of the basics. This donut breaks down the proportion of recommendations that are core SEO fundamentals versus the few AI-specific tactics.

The day Google calmed the AI gurus

I’m going to tell you something agencies hate hearing. Optimizing for Google’s generative AI still boils down to classic SEO. Clean. Structured. Demanding.

Last week, Google published a document on optimizing for its generative AI features in search. The topic set r/TechSEO on fire. The dominant reaction? « Wait, this is just normal search marketing. » Same tune from me, 9,342 kilometers away from Paris. I devour official guides with appetite. I was expecting gems. I found a reality check.

The guide details point by point what Google expects for content to show up in AI Overviews or generative answers. Crawlability. Indexability. Semantic HTML. Quality content. Authority. The killer sentence: « Optimizing for AI search is still just optimizing for the search expérience. »

The veil lifts. For the past 18 months, a flood of consultants has inundated the market with « GEO » (Generative Engine Optimization). We saw all sorts of obscure techniques bloom: special JSON-LD markup for AI, llms.txt files, chunking stratégies. Training courses. Tools. Gurus. And then Google breaks its silence. All to say: go back to the fundamentals.

I watch it every week. Between 10 and 15 e-commerce sites are draining their traffic. The ones winning with generative AI aren’t those who implemented the latest hack. They’re the ones with solid semantic architecture. With internal linking that’s actually a network, not a sieve. With content that answers precise search intents. Full stop.

So why is this guide such a slap in the face? Because it forces us to deflate the bubble of magical AI. To stop believing that a .txt file will launch your product sheets into Bard’s answer. It brings us back to the real work. The deep work.

Let me show you how.

Crawl, indexation, semantic HTML: the holy trinity

Google’s guide lists five pillars. Nothing particularly esoteric.

• Crawlability, good old Googlebot must be able to explore every product URL without obstacles. No unwanted blocks, no JavaScript acting up.
• Indexability, each page needs a clean canonical tag, an HTTP 200 status, and unique content. The basics.
• Semantic HTML, title tags, meta descriptions, properly hierarchized headings, alt attributes on images, standard structured data.
• Quality content, expertise, originality, relevance to search intent. No mention of magical length or AI-specific format.
• Authority, trustworthy external links, solid reputation. What PageRank has been rewarding since 1998.

These five pillars—I’ve been applying them in my semantic architectures since 2016. The big surprise is they haven’t changed. An LLM trained on the web ingests structured text. If your site is unreadable to a robot, the generated summary will be mediocre. If your pages are crystal clear, AI agents extract your content with surgical precision.

Concrete example. An e-commerce with 1,400 pages had multiple H1 titles, duplicate meta descriptions, and bullet lists without semantic markup. Result: zero appearances in AI Overviews. After fixing the HTML markup and strengthening internal linking, the generative modules started citing its products within 4 weeks.

No magic plugin. No AI tool. Just clean code and structure that speaks the language of machines.

3,200 pages, zero AI tool, +218% AI Overview presence

An e-commerce director calls me on a Tuesday. He invested 8,000 euros in a GEO solution. llms.txt deployed, « special snippet » markup, synthetic content « optimized for AI. » His catalog? 3,200 items. His dream? A spotlight in generative answers. After four months, he still doesn’t have a single AI Overview impression. Nothing. Traffic plateaus.

I look at his site. The problem is structure. 47% of product pages are orphaned. Canonical tags point to nowhere. H1s contain marketing phrases, not product names. No links between product sheets and catégories. The llms.txt file is a copy-paste of his XML sitemap.

We stop the spending. No more plugins. No exotic files. We restructure internal linking. We align thematic silos: each parent category feeds its subcategories. Each product sheet links back to its category. Each attribute (color, size, brand) becomes an indexable facet. HTML is cleaned up: one H1, ordered lists for specs, standard Product structured data. Not a single line of code dedicated to AI.

Six months later, the dashboard tells me a truth. 1,400 AI Overview queries captured. +218% clicks from these modules compared to near-zero at the start. Overall organic traffic jumps 23%. Generic category pages generate AI summaries with prices, ratings, and comparative attributes.

The mechanism is simple. Google’s AI crawler relies on the same signals as the classic crawler. By consolidating links between pages and making the catalog transparent, you offer a clear view of the domain. AI extracts entities, their relationships, and returns them in context. Nothing magical.

No prompt engineering. No « content chunking. » Solid foundations. A result that lasts.

llms.txt, chunking, and other gadgets: you don’t need them

Google addresses directly two trends it dismisses in one sentence. The llms.txt file? « You do not need to create special files or formats. » Content chopped into chunks (chunking)? Pointless. For the Mountain View giant, these practices aren’t a ranking factor, nor even a boost for generative agents.

It’s liberating. For a year, startups have been pushing llms.txt as the new sitemap for AI, promising miracles. Whole conferences were devoted to it. Some saw it as the way to bypass search results. Google squashes that illusion. Reality is more sober: the best file for AI is clean HTML and structured data.

I’ve audited seven e-commerce sites that had integrated these gadgets. None showed significant AI Overview traffic. On the other hand, one of my clients, a pure-play appliance retailer, never installed llms.txt. Yet it captures 82% more clicks from AI modules than its direct competitors. Its secret? Rigorous semantic cocoons, foolproof canonical URLs, and editorial content structured like a book.

Google confirmed it in its official documentation. Don’t scatter your efforts on phantoms. Focus on SEO that works.

Build a system that runs without you

Google’s guide pushes open a door many had forgotten: a site must be an autonomous system. Not a disposable content factory. An e-commerce catalog with silo-based linking, correctly filtered attributes, upward and downward links—that’s a magnet for generative AI.

I’m seeing a pattern with my clients. The ones performing in AI Overviews aren’t the ones tweaking esoteric markup all night. They’re the ones whose site « breathes » semantically. The engine quickly grasps the hierarchy of sections, the relationship between a product sheet and its accessories, how thoroughly a topic is covered by a cluster of articles.

In practice:

• Each page has a defined role in the overall architecture.
• Pillar pages (hub) connect satellite pages (spoke) via contextual links.
• Structured data reflects exactly the visible content users see, no tricks.
• Duplicate content is spotted and removed, including via canonicals.

A site I audited last week had 47% of its pages orphaned. Hundreds of product sheets with no inbound links, scattered like islands. In AI Overviews, no chance. We reconnected these pages to the mainland with relevant linking. Google started synthesizing them in its answers.

The message is clear: don’t chase gadgets. Build a coherent system. AI visibility will follow.

What if the next SEO revolution wasn’t one?

Google’s official guide had the effect of a cold shower. No magic shortcut. No secret file. Just the demand to do solid work. Crawl, index, semantics, content, authority. Five pillars, no smoke.

For an e-commerce? It changes everything. Instead of chasing the latest trend, the most profitable move is to solidify your foundations. Clean your architecture. Remove duplicates. Markup your HTML rigorously. These actions don’t buzz on LinkedIn. But Google rewards them, whether the user types their query in a classic search field or an AI one.

What if the answer was simply not changing anything dramatic? Just doing what we should have done ten years ago: a deep technical audit, a semantic overhaul, attention to detail. Classic SEO isn’t dead. It’s the only path.

So what will you stop doing today so your site becomes readable to robots—classic or generative?

Stéphane Jambu

SEO & AI Engineer

I build growth systems / AI / Neuroscience | 650+ clients · 80 LinkedIn testimonials · 30 years of expertise · 15 years of systems running without me.

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