Answer engines change the economics of visibility. A classic results page spreads clicks across ten links, but a generated answer typically synthesizes a handful of sources and ignores the rest — the difference between being cited and being invisible is binary. In practice, AEO means opening each page with a direct, quotable answer before elaborating, writing FAQ entries that stand alone when lifted out of context, adding structured data like FAQPage schema, and keeping facts consistent across your site so a model can rely on them. Vagueness gets skipped: answer engines quote pages that state something specific and checkable.
AEO overlaps heavily with generative engine optimization (GEO), and many people use the terms interchangeably. A useful distinction: GEO is the umbrella discipline of being visible to generative AI systems, while AEO targets the question-and-answer surface specifically — being the source an engine cites when someone asks "what is X" or "which tool should I use for Y." Neither replaces SEO; the same crawlable, authoritative content underpins all three. AEO just adds a sharper test to every paragraph you publish: if a model quoted this verbatim, would it be a good answer? Signals like an llms.txt file and clean heading structure help engines find and extract those answers.
Measuring AEO is where most teams stall, because there is no rank tracker for a generated paragraph. Brohns treats it as an agent's job: a GEO auditor agent scans how findable your site is when people ask AI assistants like ChatGPT or Perplexity, benchmarks you against competitors, and turns the gaps into concrete fixes — rewritten answers, missing schema, pages worth adding. Those fixes arrive as recommendations with the auditor's reasoning visible on a live timeline, not as silent changes; nothing touches your site until you have reviewed and applied it. For the step-by-step playbook, read the answer engine optimization guide.