What Is Answer Engine Optimization (AEO)?

Answer engine optimization (AEO) is writing and structuring content so that a system which answers questions directly can extract a correct answer from your page and attribute it to you. The answer engines are Google's AI Overviews, ChatGPT, Perplexity, Claude, and anything else that reads the web and replies in prose instead of handing back ten links.

Where the term sits next to SEO and GEO

Classic SEO optimizes for position in a list. Someone types a query, gets links, and decides which to click. Your job was to be high in that list and compelling enough to click.

AEO optimizes for being the material an answer is built from. There may be no list, or the list may sit below an answer that already satisfied the question. The click, if it comes, comes after the reader has already been told what you say.

GEO, generative engine optimization, is the newer term for roughly the same practice narrowed to generative systems. You will also see AI SEO and LLMO. The vocabulary has not settled, and anyone claiming a crisp technical boundary between AEO and GEO is mostly describing their own preference. In practice they name one shift: content is increasingly read by a machine that summarizes it for a human who may never load your page.

None of this replaces SEO. Answer engines are built on crawled and indexed content, so a page that is slow, blocked, or unindexed is invisible to both.

What actually changes on the page

Answer the question in the first sentence or two, in a form that survives being lifted out of context. A paragraph that only makes sense after three paragraphs of setup cannot be quoted. This is why so much AEO advice reads like old technical-writing advice: it is.

Use the question as the heading when the question is what people ask. A section titled "What is a good bounce rate for a marketing site" gets matched to that query more reliably than one titled "Benchmarks".

Keep specifics. Numbers, versions, dates, named tools, and real constraints are what a summarizer keeps when it compresses you, because they are the parts it cannot generate on its own. Vague, hedged prose compresses to nothing.

Make the facts machine-readable where the format exists. FAQPage and Article structured data, a clear heading hierarchy, and plain HTML text rather than text painted into images all help. A growing number of sites also publish an llms.txt file, a plain-markdown summary of what the site is and where the important pages are. It is a convention rather than a standard, and no major engine has committed to reading it, but it costs an afternoon.

Say what you are not. Answer engines get asked comparison questions constantly, and a page that names the cases where a different tool is the better fit is more likely to be quoted as the honest source than a page that claims to win everywhere.

The part that gets left out

Almost every AEO guide stops at the referral. The interesting question starts one step later.

A visitor who arrives from an AI answer has already read a description of what you do. They asked a question, got a summary that mentioned you, and clicked anyway, which usually means they want to verify something specific or they are ready to act. A visitor from a keyword search may be three weeks from caring.

Most landing pages are built for the second person. The hero explains what category the product is in, the next section explains why the problem matters, and the actual signup sits four scrolls down behind a form built to qualify a stranger. For someone who was pre-sold in a chat window, that is the wrong page. They are looking for the price, the constraint that disqualifies them, and the button.

Published conversion figures for AI-referred traffic vary enormously between sources, and most of them come from vendors selling AEO services, so treat them as directional at best. The point does not depend on the number. Two audiences that arrive at different funnel depths should not be shown the same first screen, and you can check which one you have.

Splitting the traffic in PostHog

Referring domain is captured by default, so you can compare the two populations without adding instrumentation:

SELECT
  if(
    properties.$referring_domain LIKE '%chatgpt%'
      OR properties.$referring_domain LIKE '%perplexity%'
      OR properties.$referring_domain LIKE '%claude%'
      OR properties.$referring_domain LIKE '%copilot%',
    'ai_assistant',
    properties.$referring_domain
  ) AS source,
  countDistinct(person_id) AS visitors
FROM events
WHERE event = '$pageview'
  AND timestamp > now() - INTERVAL 30 DAY
GROUP BY source
ORDER BY visitors DESC
LIMIT 15

Illustrative sample output:

source	visitors
www.google.com	4,120
ai_assistant	380
news.ycombinator.com	210

Then run your signup or trial event against the same split and compare the rates. Two caveats worth holding onto: some assistants send visitors with no referrer at all, so the AI bucket is a floor rather than a count, and the volumes are usually small enough early on that a week of data tells you nothing. Give it a month.

If the AI-referred group converts at a visibly different rate from search, that gap is the actionable finding, in whichever direction it points. A much higher rate says these visitors are worth building a page for. A much lower one usually says they arrived expecting something the page does not confirm quickly enough, which is a copy problem you can read for yourself by opening the page and pretending you already know what the product does.

Where to start

Take the ten pages that already get search traffic and rewrite the opening two sentences of each so the answer stands alone. That is most of AEO, and it improves the pages for human readers too, which is a useful property in a field where the ranking mechanics change every few months.