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Updated August 3, 2026
AEO 101: the loop
If you have read what AEO is, you know AI search is probabilistic and the old habits do not transfer. This page is the operating playbook: the loop a serious team runs, whether they run it with Answerworthy or by hand. AEO is a cycle, and every pass through it makes the next one sharper.
Step 1: Measure what AI actually says
Start from questions, not keywords. Build a panel of the questions your buyers really ask: informational (“what is X”), commercial (“best X for Y”), comparative (“X vs Z”), brand-aware (“is X legit”), use-case, and objection questions. A useful panel is a stratified matrix across those categories and your buyer types, roughly fifty questions to start, weighted toward commercial and comparative intent because that is where buying decisions form.
Then run the panel against the engines that matter for your audience (ChatGPT, Claude, Gemini, Perplexity). Almost everyone skips the next step: run every question multiple times per engine. Engines answer differently run to run. One sample tells you almost nothing; three to five samples with majority scoring tells you whether you are actually in the answer or just got lucky once. Record, for every answer: were you mentioned, were you cited as a source, at what position, with what sentiment, and was what it said about you true.
That last one matters more than teams expect. Engines confidently state wrong prices, wrong capabilities, and stale facts. Check every answer against an approved fact sheet of what is true about your brand.
Step 2: Diagnose why
The single most useful diagnostic in this discipline is a 2x2 between mentioned and cited:
| Cited often | Cited rarely | |
|---|---|---|
| Mentioned often | You are winning; defend and expand | Extractability or crawler problem: engines know you but cannot or will not use your pages as sources |
| Mentioned rarely | Unusual; usually a niche authority pocket | Brand-presence gap: the model barely knows you exist |
Each quadrant routes to different work. Mentioned-but-not-cited is a technical problem: check whether AI bots can reach your pages, whether your answers exist in raw HTML, whether your schema is real. Not-mentioned-at-all is a presence problem: content operations, third-party mentions, PR, patience. Doing content work when you have a crawler problem, or schema work when you have a presence problem, is how teams spend a quarter fixing the wrong thing.
Two more diagnostic joins are worth the effort. Compare your crawler logs against your citations: pages AI bots fetch heavily but never cite are your highest-leverage fix queue. And look at who is cited for the questions you lose; the winners’ content structure is the clearest brief you will ever get.
Step 3: Fix what the evidence points at
The fixes are unglamorous and they compound:
- Reachability first. Robots.txt not blocking the bots you want, no bot-wall accidentally serving challenges to legitimate AI crawlers, priority pages fetchable.
- Raw-HTML answers. The answer to the target question stated plainly in the first couple hundred words, in server-rendered HTML, not assembled by JavaScript.
- Real schema. Valid JSON-LD that matches the visible page and is substantive; thin boilerplate markup measurably underperforms no markup.
- An authored llms.txt. A curated, intent-organized guide with a short disambiguation block, not a dumped sitemap. Adoption by engines is still uneven; treat it as cheap insurance, not a silver bullet.
- Content that answers losing questions. One page per question cluster, structured answer-first, with real expertise signals.
- Third-party presence. The sources engines trust for your category (review sites, communities, trade press) shape answers even when never visibly cited.
Step 4: Verify, then repeat
One discipline separates a program from a pile of tasks: a fix is not done when it ships, it is done when a re-measurement shows it worked. Re-run the audit item, re-run the affected question cells, and compare against your baseline. Give trends at least eight weeks before drawing conclusions, and treat week-to-week wiggles as noise unless they clear a statistical threshold. When a number does move sharply, triage before celebrating or panicking: did the engine ship a model update, did you change the site, did a competitor land a big listicle, is it one engine or all of them?
Then go back to step 1. Panels evolve, engines change monthly, and the loop is the strategy.
Running the loop without losing your weekends
Everything above is doable by hand, and doing it by hand once teaches you a lot. Doing it every week, across four AI engines plus Google’s AI surfaces, with replicates, log analysis, audits, and verification, for more than one brand, is a full-time job. Answerworthy is that loop as software: panels, scheduled runs with stored raw answers, the 2x2, crawler verification, the 91-check audit, generated fixes, and verification, in one workspace with every claim tagged by its evidence.
The next rung of the ladder explains exactly how our measurement works and why you can trust it: the methodology.