Answer Engine Optimization: How to Get Cited by ChatGPT, Perplexity, and Google AI Overviews

Introduction

Answer engine optimization is the practice of structuring content so AI answer engines - ChatGPT, Perplexity, Claude, and Google AI Overviews - can extract, trust, and cite it. Where traditional SEO competes for a ranked list of links, answer engine optimization competes for a single synthesized answer and the handful of sources that answer cites. According to Gartner's 2025 guidance, organic search clicks will keep falling as generative answers absorb informational queries, which makes earning the citation more valuable than earning the rank.

What is answer engine optimization?

Answer engine optimization (AEO) is the discipline of making a passage the most extractable, verifiable, and citable source for a specific question. An answer engine like Perplexity or Google AI Overviews does not read your whole page; it selects the passage that most directly and credibly answers the query. Answer engine optimization therefore optimizes passages, not just pages: each section leads with a self-contained answer, names concrete entities, and backs every claim with checkable evidence.

Answer engine optimization vs traditional SEO

• Unit of competition: traditional SEO ranks a URL; answer engine optimization wins a cited passage inside a generated answer.
• Signal mix: classic SEO rewards backlinks and keyword coverage; answer engine optimization rewards entity authority, factual density, and answer-first structure a language model can lift verbatim.
• Measurement: rankings move in Google Search Console; answer engine optimization visibility shows up as citations in ChatGPT, Perplexity, and Google AI Overviews, which is why citation monitoring matters.

The five signals AI answer engines reward

• Entity Authority: precise references to named people, organizations, products, and standards that map cleanly to a knowledge graph. Vague language cannot be cited.
• Factual Density: the number of verifiable, distinct facts per hundred words. Answer engines prefer passages dense with checkable claims and dates.
• Answer-First Formatting: the direct answer appears before supporting context, so an extractor can quote the opening sentence.
• Source Credibility: attributable data points and citations to authoritative organizations such as Gartner, Pew Research, or primary documentation.
• Freshness: explicit recency - 2025 and 2026 figures, updated data - signals that the passage reflects current facts.

How to optimize for answer engines

• Lead every section with its conclusion, then justify it. ChatGPT and Perplexity quote the opening sentence far more often than a buried claim.
• Replace generic phrasing with named entities. 'Our platform' becomes 'the SEO AI Regent scoring engine'; 'studies show' becomes 'Gartner's 2025 analysis.'
• Add structured data. JSON-LD types such as Article, FAQPage, and Organization map entity relationships for crawlers, and an llms.txt file declares AI access at the protocol level.
• Cite primary sources with dates so an answer engine can verify the claim and attribute it to you.

Measuring answer engine optimization before you publish

Answer engine optimization is only as strong as your ability to measure it. SEO AI Regent scores every draft on two axes - a Content Score for traditional Google ranking and a GEO Score for AI retrieval - and the Review Gate blocks publishing below 70/100 Content Score AND 70/100 GEO Score. The ExplainScore breakdown names the exact signals to fix, so a writer raises Entity Authority and Factual Density before the article ever reaches an answer engine, not after.