If you've been following the chatter around "Generative Engine Optimization" or "Answer Engine Optimization," you've probably seen a flood of advice about llms.txt files, content chunking, AI-friendly schema, and prompt-style writing. Most of it is noise. Google's own documentation — updated in 2025 — cuts through cleanly, and the core message is both simpler and more demanding than the SEO industry tends to admit.
This article breaks down what Google actually says, explains what it means in practice, and highlights the parts most publishers miss entirely.
First, Understand What's Actually Powering AI Overviews
Before diving into optimization tactics, it helps to understand the engine underneath. Google's AI Overviews and the newer AI Mode aren't magic boxes that independently read the entire web. They work through two primary techniques:
Retrieval-Augmented Generation (RAG): The AI doesn't generate answers from memory alone. It queries Google's core Search index — the same one used for blue-link results — fetches relevant pages, reads them, and generates a response grounded in that retrieved content. The clickable source links you see in AI Overviews are the pages that were actually retrieved.
Query fan-out: When someone searches for something complex, the AI doesn't just process the literal query. It generates multiple related sub-queries simultaneously. A search like "how to recover from a Google core update" might fan out into "signs your site was hit by a Google core update," "content quality signals Google uses," and "how long do core update recoveries take." Each fan-out query pulls its own results.
Why this matters: It means there's no separate "AI index" to get into. If your page ranks well in organic search and is crawlable with a snippet, it's already eligible for AI Overviews. You're not optimizing for a different system — you're optimizing for the same Search infrastructure you already know.
The Real Divide: Commodity vs. Non-Commodity Content
This is the single most important concept in Google's guidance, and it's the one most often glossed over.
Commodity content is information that could originate from anyone. "10 cybersecurity tips for small businesses." "What is phishing?" "How to create a strong password." These topics have been covered thousands of times, the information is widely known, and a generative AI model could produce them without consulting your site at all. If your content falls into this category, AI systems have no particular reason to cite you — they can simply generate the answer themselves.
Non-commodity content has something commodity content lacks: a reason to exist that's specific to you. A security researcher's first-hand analysis of a zero-day they discovered. A breakdown of an incident your team responded to. A documented test comparing five password managers using criteria you defined. A breach post-mortem with root cause analysis that only the affected organization could provide.
The difference isn't just about depth. It's about whether your content contains information that exists only because you produced it. First-hand experience, original research, proprietary data, expert analysis of primary sources — these are signals that your content adds something to the web rather than duplicating what's already there.
Consider the example Google itself provides: "7 Tips for First-Time Homebuyers" (commodity) vs. "Why We Waived the Inspection & Saved Money: A Look Inside the Sewer Line" (non-commodity). The second piece has a perspective that can't be replicated — the author was there, made a specific decision, and is reporting the outcome. AI can't fabricate that. It can only cite it.
For cybersecurity publishers specifically, a news article that simply rewrites a vendor advisory is a commodity. A piece that adds timeline analysis, compares the vulnerability to a prior incident, reaches out to affected parties for comment, or provides reproduction steps from independent testing — that's non-commodity.
What AEO and GEO Actually Mean (And Why Google Disagrees With the Industry)
The SEO industry has spawned two new acronyms to describe optimization for AI systems: AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization). Consultants have built entire service lines around these terms.
Google's official position: these are just SEO. The same signals. The same systems.
This is more significant than it sounds. It means Google is explicitly pushing back on the idea that you need a separate strategy for AI search. The implication is that anyone selling you an "AEO audit" distinct from a standard SEO audit is selling you something Google doesn't recognize as real.
That said, there's a practical nuance worth noting: while the underlying signals are the same, the emphasis shifts when AI is synthesizing answers. Traditional SEO rewards pages that match query intent and have authority signals. AI Overviews also reward pages that are citable — meaning their content is structured clearly enough that the AI can extract a specific claim, quote an explanation, or attribute a data point. A page can rank well in organic search but never appear in an AI Overview if its content isn't written in a way that's easy to reference directly.
The distinction isn't about a different algorithm. It's about readability and extractability — which are good writing practices anyway.
The Myth-Busting Section: Things You're Wasting Time On
Google's documentation explicitly names several practices circulating in the industry as unnecessary or ineffective for Google Search. Here's the list with added context on why each one falls short:
llms.txt files
This format, borrowed from robots.txt conventions, was proposed as a way to give AI systems a structured summary of your site. Google says directly: " You don't need this. Google can crawl and index many file types, but no special file type gets you preferential treatment in AI systems. For non-Google AI crawlers (like those from Anthropic, OpenAI, or Perplexity), llms.txt may eventually matter — but for Google, it's currently irrelevant.
"Chunking" content
Some advice tells you to break pages into small, discrete answer blocks so AI can extract them more easily. Google explicitly says this isn't required. Their systems can understand which part of a longer page is relevant to a query. Write for your readers. If shorter pages make sense for your topic and audience, great. If a 3,000-word technical deep-dive serves your readers better, that's fine too.
Rewriting content in "AI-friendly" language
You don't need to adopt a Q&A format, use specific trigger phrases, or rephrase everything as direct answers. Google's AI understands synonyms and semantic intent. If your content genuinely answers a question, the system can figure that out without you gaming the phrasing.
Chasing inauthentic "mentions"
Some practitioners advise seeding forums, comment sections, and third-party blogs with brand mentions to influence AI responses. Google's spam systems catch this, and the generative AI features inherit the same quality filters. Unearned mentions don't help; they may hurt.
Overloading structured data
Structured data (schema.org markup) remains useful for rich results — it helps Google display reviews, FAQs, products, and events properly in traditional search. But there's no special schema that gets you into AI Overviews. Don't add schema, specifically hoping it unlocks AI features; it won't.
What Actually Moves the Needle
Strip away the myths, and you're left with a short, unglamorous list:
1. Be crawlable and indexable with a snippet enabled
Pages blocked by noindex, those behind login walls, or those with no-snippet directives can't appear in AI Overviews regardless of content quality. This is table stakes. Run a Search Console coverage audit first.
2. Produce genuinely first-hand or expert content
The AI is looking for pages that contain information it can't synthesize on its own. Reviews written after actual use. Analysis from someone with domain expertise. Data from your own research. If your content could be generated by an AI without consulting your site, it probably won't be cited by one either.
3. Write clearly, with a navigable structure
Headings that describe what a section covers. Paragraphs that contain one idea each. Sentences that say what they mean without filler. This isn't about writing "for AI" — it's about writing well. The extractability that makes AI Overviews cite your content is the same thing that makes human readers trust it.
4. Use high-quality images and video where relevant
AI Overviews can surface image and video results, not just text. If your topic benefits from visual illustration — a hardware teardown, a vulnerability diagram, a product comparison screenshot — include original visuals with accurate alt text and descriptive filenames.
5. Ensure good page experience
Core Web Vitals, mobile rendering, and low latency. These remain ranking signals, and they affect whether retrieved pages get surfaced prominently in AI responses.
If You Use AI to Help Write Content: What Google Actually Requires
This is where many publishers are nervous, and the guidance is worth reading carefully.
Google does not ban AI-assisted content. What it penalizes is scaled content abuse — producing large volumes of pages without adding value for users. The test isn't whether AI was involved in writing. The test is whether the output meets the same quality and usefulness standards Google would apply to any content.
In practice, this means:
- Using AI to draft a structure, then filling it with first-hand knowledge, original analysis, and expert commentary: acceptable, and likely fine.
- Using AI to generate 500 pages of product descriptions with no human review or added value violates the spam policy.
- Using AI to speed up research or improve phrasing on content you've substantially developed yourself: acceptable.
- Using AI to spin existing articles into slightly different versions at scale: spam policy violation.
The guidance also notes that transparency is a good practice. If your publication process involves AI tools in meaningful ways, explaining that to readers (in a site-level disclosure or per-article note) builds trust. For e-commerce specifically, AI-generated images must include IPTC DigitalSourceType metadata, marking them as algorithmically produced.
The Agentic Web Is Coming — Here's What to Watch
Beyond AI Overviews, Google's documentation introduces something worth tracking: agentic experiences. AI agents — systems that can book reservations, fill out forms, compare products, and complete tasks autonomously — are beginning to access websites the same way browsers do, by rendering pages, reading the DOM, and interpreting accessibility trees.
Google points to the Universal Commerce Protocol (UCP) as an emerging standard for how agents will interact with commerce sites. This isn't mainstream yet, but it signals where things are going: a web where the "user" visiting your site may be an AI agent acting on someone's behalf, not a human at all.
For publishers, this is mostly future-watch territory. For e-commerce operators, it's worth considering now: your checkout flows, product data structures, and schema markup will increasingly be navigated by agents rather than read by humans. Semantic HTML, clean DOM structure, and good accessibility practices aren't just for screen readers — they're also how agents parse your pages.
The Practical SEO Checklist for AI Search Readiness
For website owners who want a concrete action list:
Content audit:
- Identify your top-traffic pages. Ask honestly: does this page contain information that exists because we produced it, or is it a restatement of commonly available facts?
- Flag commodity pages for upgrading with first-hand data, original examples, or expert commentary.
Technical audit:
- Check Search Console for indexing issues, noindex tags on content you want crawled, and coverage errors.
- Verify snippets aren't blocked via X-Robots-Tag or meta robots directives.
- Review Core Web Vitals, particularly LCP and CLS.
Content creation:
- Build a process for producing non-commodity content: primary source analysis, original interviews, first-hand testing, proprietary data.
- Stop creating "answer" pages that duplicate what's already on a dozen other sites.
AI tools policy:
- Decide your publication's approach to AI-assisted writing and document it.
- Ensure any AI-assisted content goes through substantive human review that adds real value before publication.
Ignore:
- llms.txt, content chunking, AI-specific schema, inauthentic mention campaigns.
Final Thought: The Bar Just Got Higher
The honest takeaway from Google's guidance is that the bar for content that earns visibility in AI-powered search is meaningfully higher than the bar for traditional organic ranking. An article that ranks #3 for a moderately competitive query by satisfying on-page signals might never be cited in an AI Overview if it lacks a distinctive perspective.
That's not necessarily bad news. It's a forcing function. The content that survives this shift is the content that was always worth creating: original, authoritative, specific, and written with a real reader in mind. Publishers who've been producing commodity content at scale are the ones with the most to worry about.
For those doing genuine editorial work — original reporting, expert analysis, first-hand testing — the AI era may actually favor them over the SEO optimization shops that dominated the last decade.
Sources: Google Search Central — Optimizing your website for generative AI features on Google Search and Google Search's guidance on using generative AI content