Vice President · TheGreyMatter.ai
In brief
Publish useful, verifiable answers on accessible pages. Google says its existing SEO foundations apply to AI search features, with no special AI file or schema required. Discovery and citation remain platform decisions.
Key takeaways
- Answer a real question with enough evidence to support the answer.
- Make content accessible, internally linked, and accurately described.
- Measure discovery and useful reader actions without promising AI citations.
SEO usually refers to helping search engines discover and understand pages. AEO, or answer engine optimization, emphasizes answering questions clearly. GEO, or generative engine optimization, emphasizes visibility in generated answers. These labels overlap, and they do not describe one shared technical specification. The useful question for a publisher is what makes a page worth finding, understandable in context, and trustworthy enough for someone to use.
Start with what search platforms actually document
Google says the established SEO foundations remain relevant to AI Overviews and AI Mode. It does not require a special AI text file or special structured data for those features. A supporting page must be indexed and eligible to appear in Search with a snippet. Meeting eligibility requirements does not guarantee that a page will be selected.
Keep the scope of that statement clear: it is Google's guidance for Google's features. Other services can use different crawlers, indexes, retrieval systems, and display rules. A claim that one formatting trick guarantees visibility across every AI assistant needs evidence about each service. Treat unexplained ranking promises as hypotheses to test, not requirements to build into a publishing process.
Write something that adds an answer
Google's guidance emphasizes useful content and a clear reason for a reader to choose a page. For an editorial team, that means selecting a question it can answer with a concrete explanation, original analysis, or a well-labeled worked example. Repeating the same advice across many slightly different titles does not create a stronger reference library.
Consider a hypothetical founder searching for RAG versus fine tuning. A useful article can define both approaches, compare the problem each addresses, and show an example involving a changing policy library. It should state the limits of the comparison, cite technical sources, and explain which recommendation is editorial judgment. An invented customer outcome or a fabricated expert endorsement would weaken the evidence, however polished the page looks.
Use a direct opening answer, then develop the reasoning. Add a comparison table when readers need to weigh parallel choices. Include common questions when the answers clarify actual uncertainty. These are reading choices, not a demand to force every article into identical fragments for an imagined machine preference.
Make every useful page reachable
Google's SEO Starter Guide describes how links help discovery and how descriptive titles and link text help people and search engines understand a page. Apply that foundation to the content people actually need: the main answer should be present as readable text, and related pages should connect through ordinary links.
For this site's editorial workflow, a new article belongs in the archive, a relevant topic guide, the sitemap, and the feed. Its canonical URL should identify the preferred page, while its title and description should accurately describe its contents. A checklist should also catch broken links and accidental indexing restrictions. Publishing many pages that cannot be reached from the site's own navigation makes the library harder to use.
- Give the page a specific title that matches its actual answer.
- Place the main explanation in accessible page text.
- Link from a relevant topic guide and related articles.
- Check the canonical URL, sitemap entry, and intended indexing settings.
- Verify that source links and internal reading paths work.
Measure useful discovery instead of chasing guarantees
Begin with observable publishing checks: can a visitor reach the page, does the intended content render, and do the canonical URL and source links resolve? Then examine available search performance data alongside meaningful reader actions, such as reading a related guide or opening a relevant project page. Define those actions before increasing publishing volume.
If you sample AI answers, record the service, date, exact question, returned answer, and any cited URLs. Use a consistent set of questions and treat the results as directional observations. Answers can vary, and a single mention is not proof of stable ranking or attributable traffic. Maintain the page because its information remains useful and correct, then use evidence to decide which unanswered reader questions deserve the next article.
From reading to doing
Put it to work: improve one answer before publishing another page
Pick one question your intended reader actually needs answered. For a hypothetical AI evaluation guide, the question might be what evidence a team needs before expanding a pilot. Read the page as a visitor arriving directly from search. The opening should answer that question, explain its limits, and make the supporting example easy to find without requiring the visitor to browse the rest of the site.
Create a small before-and-after record. Note the question, the useful answer currently missing, the evidence added, and the reader action the change should support. A more specific worked example or an honest source note can improve the page even when its word count barely changes. The worksheet is an editorial review aid, not a score that predicts search ranking or inclusion in an AI answer.
- Write the reader's question in ordinary language. Check that the title and first useful paragraph address the same need.
- Separate facts, proposed methods, and illustrative examples. Link material factual claims to sources that support their actual scope.
- Add the missing decision detail: a comparison, a worked example, an exception, or a practical next step. Remove repetition that delays the answer.
- Check the live page's readable text, links, canonical address, and relevant indexing settings. Keep visible content and structured data consistent.
- Record the revision and review its performance over an appropriate period using the measurements available to you. Treat a single generated answer as an observation, not a stable ranking.
Common questions
Is llms.txt required for AI search visibility?
Google explicitly says special AI text files are not required for its AI search features. An optional navigation file is not a universal requirement, a ranking guarantee, or a replacement for crawlable pages.
Can AI assisted articles be useful for SEO?
They can be useful when they answer real questions accurately and provide value. Check sources, identify editorial analysis, disclose the production process where relevant, and avoid fabricated experience or repetitive pages created only to target keywords.
Can a website guarantee that an AI assistant will cite it?
No. A publisher controls its content, accessibility, and presentation. Selection and citation depend on the service and query. Track observed results without presenting them as guaranteed future placement.
Sources and evidence
AI assisted editorial analysis. Primary sources checked October 10, 2026. The worked examples and proposed review procedures are editorial illustrations, not reported customer results, vendor guarantees, or claims of personal experience.
One useful next step: Choose one idea from this note and test it at the smallest scale that could teach you something this week.
