#Ask ChatGPT about your own product
Here's an experiment that ruins somebody's afternoon every week: open ChatGPT, ask about your own product category, and read what it says. Half the time it describes a competitor - accurately, even lovingly, citing their docs. Your product, which you know is better for this exact query, gets nothing. Not a criticism. Silence.
Nobody out-marketed you. Nobody out-wrote you. You simply don't exist in a form the machine can use. Your launches live in a widget behind a login, your changelog is a JavaScript shimmer, your best explanations are in tweets. The AI didn't choose your competitor. It chose the only contestant that showed up as a document.
This guide is the recipe for showing up: how product updates get cited by Google's AI Overviews, ChatGPT, Perplexity, and whatever arrives next quarter. No hacks, no "AI SEO" mysticism - just the boring mechanics of being extractable.
#Why AI cites what it cites
Strip away the acronyms and every answer engine does the same thing: it retrieves candidate pages, then lifts the clearest passage that answers the question. Citations go to pages that make both steps easy:
Retrievable. Indexed, crawlable, fast, with URLs that don't change when marketing redesigns the site. If the content only exists after four frameworks hydrate, most agents never see it. Server-rendered HTML is the whole game at this layer.
Extractable. Self-contained answer blocks near the top. Headings phrased like the questions people actually ask. Tables for comparisons, lists for processes, one idea per paragraph. A 40-to-80-word passage that works with zero surrounding context is the atomic unit of citation.
Credible. Named authorship, visible dates, real statistics with sources, transparent corrections. AI systems weight recency and attribution heavily - undated, unsigned content loses to dated, signed content on the same facts.
That's it. AEO, GEO, LLMO - different hats on the same three requirements. Google says it plainly in its AI features guidance: optimizing for generative search is still SEO. The engines that aren't Google reward the same structure even more aggressively.
#The playbook, in order
One permanent URL per release. Everything downstream depends on this. A launch that lives at /update/[id] can be crawled, linked, dated, and cited. A launch that lives in a widget state cannot. If you do nothing else in this guide, do this.
Answer first, context after. Open every launch and guide with the direct answer - 40 to 80 words a stranger could quote. Details, background, and narrative go below. This isn't dumbing down; it's respecting that both scanners and machines read top-down.
Headings that match real queries. "What a launch home actually is" beats "Our philosophy" - the first is retrievable, the second is decoration. Every H2 should be a question someone typed; every H3, its sub-question.
Tables beat prose for comparisons; lists beat paragraphs for processes. Engines lift structured formats almost preferentially. Anywhere you'd write "on the one hand," consider rows and columns instead.
Schema that describes, not decorates. Article with real dates and word counts. BreadcrumbList mirroring your URLs. FAQPage built from genuine questions. Organization with sameAs links so entity resolution works. Validate at validator.schema.org - markup that lies about the page is worse than none.
An llms.txt that lists the corpus. A small index file at your domain root pointing agents at your key pages, plus the full text as llms-full.txt for single-fetch ingestion. See the spec at llmstxt.org. This is the cheapest agent-readiness win in existence.
Freshness as a habit. Visible "last verified" dates, quarterly refreshes of competitive pages, current-year statistics. AI systems weight recency hard - a maintained page beats a better abandoned one.
Internal links as a graph. Hub pages linking spokes, spokes linking back and sideways, releases linking feedback and docs. Link equity is how small sites borrow authority from their own best pages.
#What not to do
Three traps, all popular, all harmful. Don't write separate content "for AI" - same pages serve people and machines, and forked variants risk spam policy trouble. Don't dice pages into fragment soup for imaginary chunking algorithms - normal headings and paragraphs are what every engine parses. And don't bulk-generate thin variations at scale - AI content is fine when it's genuinely helpful; manufactured scale is what policies punish.
#What I'd do Monday morning
- Pick your five most important queries and run each through ChatGPT, Perplexity, and Google. Record who gets cited. That's your baseline, and it takes an hour.
- Give your top ten pages the extractability pass: answer-first lede, query-shaped headings, one comparison table where prose currently rambles, visible dates.
- Ship llms.txt plus the full corpus file. An afternoon's work that every future agent thanks you for.
- Re-check monthly. AI answers are non-deterministic - run each query several times and track citation rates, not single runs.
Our launch feed and every guide on this blog are built this way - permanent URLs, answer-first structure, real dates, machine-readable companions. Eat your own mechanics.
#FAQ
#1. Is GEO different from SEO?
No - it's SEO with the extraction step made explicit. Google's own position: generative AI search runs on the same ranking and quality systems as classic search. Optimize for people and core search, structure for machines, and both improve together.
#2. Does llms.txt make AI cite you?
Not by itself - no engine has confirmed it as a ranking lever. It's discovery infrastructure: it helps agents find and ingest your content efficiently. Pair it with content worth ingesting.
#3. How long until AI starts citing a new page?
Faster than classic SEO ever moved - fresh content on retrieved surfaces can surface within days, sometimes ~24 hours. The bottleneck is almost always retrievability (indexing, rendering), not authority.
#4. Should we rewrite everything for AI?
No. Fix structure, not voice. Headings, answer blocks, tables, dates, schema - the packaging. If the underlying content is thin, no packaging saves it; if it's good, packaging is all it lacked.
#Related reading
- Best changelog tools: launches worth citing, compared.
- Product Hunt alternatives: why permanent pages beat spikes.
- Incident.io alternatives: timelines engines can quote.
- Best documentation platforms: docs agents can parse.
- Launch feed: the mechanics applied, live.