GEO stands for Generative Engine Optimization. It is the practice of making web content easier to find, understand, and use as a source in AI-generated answers.
Google is unusually clear about this. Its current guidance says that the same SEO practices apply to AI Overviews and AI Mode. There are no extra technical requirements, no special AI schema, and no machine-readable AI file required for inclusion.
A useful model, not a platform specification
I still find four verbs useful when explaining GEO: crawl, parse, synthesize, and cite. They describe the path from a published page to an answer with links. They are a mental model, not an official process promised by every platform.
Crawl. A search engine or AI service must be able to fetch the page. For Google Search and its AI features, the relevant crawler is Googlebot. This corrects a common mix-up with Google-Extended. Google says that Google-Extended controls some Gemini training and grounding uses. It does not control inclusion or ranking in Google Search.
Parse. The fetched page needs useful text, sensible headings, internal links, and metadata that matches the visible content. Structured data can help a search engine understand eligible page types, but it should describe what a visitor can actually see. Adding schema does not make a weak page authoritative, and Google does not require special schema for AI Overviews or AI Mode.
Synthesize. An AI search feature may gather material from several pages and compose an answer. Google says AI Overviews and AI Mode can use query fan-out, which means running several related searches across subtopics and data sources. The systems and links can vary between responses.
Cite. Some generated answers show supporting links. Microsoft now exposes part of this activity in the public-preview AI Performance report in Bing Webmaster Tools. It reports citations, cited pages, and sampled grounding queries across supported Microsoft AI experiences. Microsoft also warns that those numbers do not show ranking, authority, or placement inside an answer.
There is no fixed number of citations to target. A citation seen in one answer is useful evidence, but it is not a stable rank.
What the platforms officially recommend
Google's advice starts with normal search hygiene. The page must be indexed and eligible to appear in Search with a snippet. Google recommends allowing crawling, using internal links, keeping important content in text, providing a good page experience, and making structured data match the visible page.
Google also says you do not need llms.txt or another AI text file to appear in its AI search features. That does not make llms.txt useless. It means the file is optional and must not be presented as a Google requirement.
Microsoft's current guidance is similar in spirit. Bing recommends clear structure, evidence for claims, accurate updates, and consistent information across formats. It also recommends sitemaps and IndexNow to help participating search engines discover additions, changes, and removals sooner. Faster discovery still does not promise a citation.
This is the clean line I use: the platforms document crawlability, indexing, content controls, and measurement. Advice such as opening with a direct definition, writing answer-first paragraphs, or adding a short FAQ where readers need one is community GEO practice. It can make a page easier for people and machines to understand, but the platforms do not promise that a particular format will be quoted.
What Lovable handles today
Lovable now has an SEO & AI search area under Services. Its review checks code, a preview deployment, and, after publication, the live site. The checks include robots.txt, sitemap.xml, metadata, canonical URLs, semantic structure, structured data, llms.txt, performance, accessibility, indexing, and AI Markdown rendering.
The product can apply many fixes through Try to fix, but the scan is not automatic when you publish. Lovable says to run the review again after changes and after publication.
Lovable's AI Markdown check is positive-only. A passing result confirms that Lovable serves a clean Markdown version to AI crawlers. It does not confirm indexing or citation. The same limit applies to llms.txt: Lovable can create and validate the file, while Google says the file is not required for its AI features.
A practical check for a Lovable site
Start in Services → SEO & AI search and run Scan project. Fix anything that genuinely blocks discovery, such as a sitewide noindex, an unreachable home page, or crawler rules that block the live site. Publish the changes, then scan again so the live checks can run.
Open these URLs yourself:
https://your-domain.example/robots.txt
https://your-domain.example/sitemap.xml
https://your-domain.example/llms.txtCheck that they return the intended content on the correct domain. Then inspect an important article with a normal HTTP request:
curl -L https://your-domain.example/walkthroughs/what-is-geoThe response should contain the useful page content, not only an empty application shell. A custom user agent can help you compare responses while debugging, but it does not prove how a platform indexes the page.
For Google, connect Search Console, verify the site, submit the sitemap, and use URL Inspection when a page is missing. Lovable can guide this setup when its Google Search Console connector is enabled for the workspace. For Bing, verify the site in Bing Webmaster Tools, submit the sitemap, and consider IndexNow for pages that change often.
After that, improve the page itself. Answer the main question early. Use headings that tell the reader what follows. Back factual claims with primary sources. Update the page when the facts change instead of changing only the date.
That is a sensible GEO workflow: make the page accessible, make it clear, verify the live result, and measure what the platforms actually report.
Sources
- Google Search Central: AI features and your website (https://developers.google.com/search/docs/appearance/ai-features)
- Google Crawling Infrastructure: Google's common crawlers (https://developers.google.com/crawling/docs/crawlers-fetchers/google-common-crawlers)
- Microsoft Bing Webmaster Blog: Introducing AI Performance in Bing Webmaster Tools Public Preview (https://blogs.bing.com/webmaster/February-2026/Introducing-AI-Performance-in-Bing-Webmaster-Tools-Public-Preview)
- Lovable documentation: Optimize your app for SEO and AI search (https://docs.lovable.dev/features/seo-aeo)