I'm a brand strategist, not a developer — but every campaign I run eventually turns into a conversation with someone's engineering team. Over the past year, that conversation has shifted. It used to be about meta tags and sitemaps. Now it's about whether a site is even readable by the models powering AI answers. Here's the technical breakdown I actually walk dev teams through.
SEO (Search Engine Optimization)
This is the one most engineers already know. Crawlable HTML, clean URL structure, fast Core Web Vitals, valid schema.org markup, an accurate sitemap.xml and robots.txt. The mechanics haven't changed much — what's changed is how much weight structured data carries now, because it's the same markup that AEO and GEO systems lean on.
AEO (Answer Engine Optimization)
This is about formatting content so it can be lifted directly into a featured snippet or a voice/chat answer. Practically, that means: a direct, self-contained answer to the implied question within the first 1-2 sentences of a section, genuine FAQ schema (FAQPage in JSON-LD, not just visually-styled accordions), and heading structure that maps to actual questions people ask, not just keyword strings. If a section can't be understood correctly when read on its own, out of context, it won't get picked up.
GEO (Generative Engine Optimization)
This is the newest layer, and it's aimed at large language models rather than traditional crawlers — think AI Overviews, Perplexity, ChatGPT's browsing mode. A few things I've seen actually move the needle here: an llms.txt file at the root (still informal, not a ratified standard, but increasingly respected), consistent factual claims about an entity across every page and every third-party mention (NAP consistency isn't just a local-SEO thing anymore, it's an entity-recognition thing), and content that states things plainly rather than burying them in marketing language — generative models tend to extract and cite the most unambiguous sentence in a block, so ambiguity is what gets you skipped.
Where they overlap
The overlap between all three is bigger than most teams assume: clean semantic HTML, structured data, and unambiguous writing serve all three systems at once. The place I most often see teams over-invest is chasing GEO-specific hacks while their basic schema markup is still broken. Fix the foundation first.
Curious how other developers here are handling llms.txt and structured data for AI-crawler visibility — is anyone treating this as a standard part of a build now, or still experimental?
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