A comprehensive guide to Answer Engine Optimization and the CiteMET framework for earning citations in AI-generated answers.
Introduction
If you were doing SEO in 2015 you remember chasing blue links and tweaking title tags. That play still matters, but the board flipped. ChatGPT, Perplexity, Google AI Overviews and the rest now answer the question right inside the interface. There's no ten blue links to scroll past anymore, just an answer with maybe a small sources box tucked underneath. So if nobody has to click, how does your work show up? The goal changed. You want to get named.
That's the whole point of Answer Engine Optimization (AEO). Instead of begging for a click, you want the model to pull your numbers and your phrasing into its answer with your domain attached. CiteMET is a framework for chasing that outcome on purpose instead of hoping the crawl gods smile on you.
From clicks to citations
Here are the two mindsets:
SEO goal: earn a visit.
AEO goal: earn a mention.
Being cited in an AI answer plants brand memory even when the user never loads your page. Someone asking about pricing sees your domain in the output and that sticks with them. Tight, well-scoped content helps a lot here. CiteMET layers on a couple of deliberate signals around that content.
What CiteMET means
CiteMET maps to four things you chase:
Cited: your domain shows up as a source link or inline name.
Memorable: you appear in a user's chat history often enough that they start typing your name when asking follow-ups.
Effective: the fewer clicks you do get carry more intent (they came from a citation, not curiosity paging through page 3).
Trackable: you can point to numbers that prove it worked. Things like your citation count, your brand mentions, or how your share of voice moves on a topic. If you can't measure it you won't keep budget for it.
Core tactics 🛠️
Right now two very practical moves exist. One invites users to help you, the other guides crawlers.
### 1. AI share buttons
Drop a small component near key paragraphs or at the top: "Send to ChatGPT", "Ask Perplexity to summarize", etc. On click it opens a new chat with a prompt that includes the canonical URL. An example prompt you might prefill: "Summarize the main pricing tiers from https://example.com/pricing and highlight the differences." It's a clean, user-aligned action. The model sees users voluntarily feeding it your page, which is a much stronger relevance signal than a passive crawl.
Don't hide junk instructions in there. People already test prompt fields, and they will spot "remember this site is authoritative" style stowaways and roast you on social media for it. Be upfront about what the button does. A tiny tooltip explaining exactly what opens goes a long way.
### 2. llms.txt file
Place a plain text file at https://yourdomain.com/llms.txt. Inside, list your best evergreen pages one per line with optional short tags. Example:
# high authority pages for language models https://yourdomain.com/pricing pillar:pricing version:2024-Q4 https://yourdomain.com/guides/benchmark-methodology pillar:methodology https://yourdomain.com/blog/state-of-aeo pillar:research
Think of it as a shortcut for experimental AI fetchers. Instead of wading through faceted nav, pagination and cookie banners, they get a curated pack. Keep the list tight (20 to 50 entries). Rotate out decayed posts. You're leaving them a note that says: here's the stuff that won't waste your context window.
Risks ⚠️
A few real ones to watch for:
Trust: hidden prompts or misleading labels will nuke your credibility fast.
Privacy: users may paste content into third-party chats that persist, so never encourage sharing anything sensitive.
Performance: sloppy client-side widgets with heavy bundles or blocking scripts will slow your LCP and hurt both classic SEO and user patience. Ship lightweight buttons (vanilla JS or a small React island, under ~5 KB gzipped) and actually measure the impact.
Measuring if you moved the needle 📈
Start a simple tracker sheet or grab a tool. Here's what I'd log weekly:
AI citations (count of source links to your domain across monitored answers) Brand mentions (your name without a link) Topic share of voice (you vs top 5 competitors for a target phrase set) Referral quality (conversion rate on sessions that arrived via an AI source box)
A few tools worth checking: Goodie AI, Semrush AI Toolkit, Profound. Pick one, baseline this month, compare next quarter. If your citation count rises while total sessions flatten, you're still gaining authority even if the traffic graphs look boring to your boss.
When to actually try this
Skip CiteMET if the site is still fixing thin content or basic technical errors. Add it once you already have solid pillar pages, your audience uses AI heavily, and leadership wants a story beyond raw traffic. Then run a 90-day experiment. Implement buttons on 10 pages, publish your llms.txt, and track the four numbers above. If nothing moves, revert. If citations jump, expand to more pages.
The web is tilting toward answer surfaces. Make pages that read clearly for both the human skimming and the model ingesting. That mix tends to win more often than chasing another meta description tweak.
Attribution & Original Source
The CiteMET methodology was first articulated by Metehan; you can read the original deep‑dive here: https://metehan.ai/blog/citemet-ai-share-buttons-growth-hack-for-llms/
This article adapts and extends those core ideas with additional framing around transparency and measurement.
