SEO & AEO Audit Checklist: 25 Factors AI Engines Use to Cite Content

Answer Engine Optimization (AEO) is a younger discipline than SEO, and the signals AI engines use to select sources aren't published anywhere the way Google's ranking factors are. But they're not a mystery either. By analyzing citation patterns across ChatGPT search, Perplexity AI, Google AI Overviews, and Gemini, a clear picture emerges of what separates cited content from ignored content.

This SEO AEO checklist covers 25 of those factors, organized into seven categories. Run it against any page you want AI engines to cite. Each factor that's missing is a specific gap you can fix.

Category 1: Crawl and Access (Factors 1-4)

Before any content quality signal matters, AI engines have to be able to reach and read your page. These four factors are gates - if any one of them fails, nothing else on this list matters.

• 1.AI crawlers are allowed in robots.txt - Check that GPTBot (OpenAI), PerplexityBot (Perplexity), ClaudeBot (Anthropic), and Googlebot are not blocked by Disallow rules, either individually or by a broad 'User-agent: *' block.
• 2.Content renders server-side - If your page requires JavaScript to display its content, AI crawlers that don't execute JavaScript will index a blank page. Verify that core content is visible in the raw HTML response.
• 3.Page loads in under 2.5 seconds - Crawlers deprioritize slow pages and may abandon crawls before fully indexing them. An LCP (Largest Contentful Paint) under 2.5 seconds is the target.
• 4.No noindex tags on target pages - A 'noindex' meta tag or X-Robots-Tag HTTP header tells all crawlers to exclude the page from their index. Verify these aren't present on pages you want cited.

Category 2: Content Structure (Factors 5-9)

AI engines extract content section by section. How you structure information within each section determines whether the AI can use it - and whether it will.

• 1.Each section leads with a direct answer - The first 40-80 words of every H2/H3 section should contain the direct answer to the question that section addresses. Content that builds context before answering is systematically deprioritized by AI retrieval systems.
• 2.H2 and H3 headings are written as questions or direct answers - Headings like 'What is atomic fact density?' or 'How AI engines select sources' are easier for retrieval systems to match to user queries than vague headings like 'Overview' or 'Background'.
• 3.Sections are short enough to be extracted independently - A section that runs 600+ words on a single heading is harder for AI to parse and cite than two or three shorter, focused sections. Each section should be self-contained.
• 4.No long preambles before the main content - The first paragraph of the page should define the topic immediately, not spend time setting the scene. AI engines use the opening of a page to determine relevance before reading further.
• 5.Lists and tables are used for multi-part answers - When an answer has multiple components, a numbered list or table communicates structure that an AI can extract intact. Prose that buries a five-step process in paragraph form is harder to synthesize.

Category 3: Atomic Facts and Factual Quality (Factors 10-13)

AI engines synthesize responses from the facts they extract. Pages with more extractable, verifiable facts are selected more often - not because length matters, but because there's more usable material per section.

• 1.At least 3-5 atomic facts per 200 words - An atomic fact is a discrete, self-contained, verifiable statement. 'Perplexity AI was founded in 2022' is an atomic fact. 'Perplexity is a leader in AI search' is not. Count the verifiable claims in each section - if there are fewer than three, the section is likely too thin to be cited.
• 2.No unverifiable marketing claims masquerading as facts - Phrases like 'industry-leading', 'best-in-class', and 'revolutionary' are not facts. AI engines are tuned to prefer specific, verifiable claims over vague superlatives. Replace them with specific numbers, dates, or named comparisons.
• 3.Statistics and data include their source or context - A statistic cited without context ('studies show...') is less citable than one with specific attribution ('a 2025 Ahrefs study found...'). Precision increases credibility with AI retrieval systems.
• 4.Claims are consistent across the page - AI engines cross-reference claims within a page when building responses. Internal contradictions - stating two different numbers for the same metric, for example - reduce the reliability score of the entire page.

Category 4: Entity and Semantic Clarity (Factors 14-17)

AI engines build knowledge graphs from the content they index. The clearer your entity definitions and relationships, the more reliably your content is retrieved for relevant queries.

• 1.The main entity is named and defined within the first 100 words - The page should establish what it's about immediately. If you're writing about your product, name it and describe what it does in the opening paragraph. Don't assume the reader knows context from a previous page.
• 2.Named entities are used instead of pronouns throughout - 'Perplexity AI' instead of 'it', 'Google AI Overviews' instead of 'the feature'. When content is extracted out of context for AI synthesis, pronoun references lose their meaning. Named entities retain it.
• 3.Related entities and concepts are present in the content - A page about AEO that never mentions ChatGPT, Perplexity, structured data, or schema will score lower on semantic completeness than one that covers the topic's full conceptual landscape. AI engines expect related entities to appear in content about a topic.
• 4.Entity relationships are stated explicitly - Don't leave AI engines to infer that Perplexity AI is an answer engine, or that FAQPage is a type of schema.org structured data. State relationships directly. 'FAQPage is a schema.org structured data type...' is clearer than 'FAQPage...' with context implied.

Category 5: Structured Data (Factors 18-21)

Structured data is the clearest signal you can send to AI engines. It converts your content from prose that needs to be parsed into machine-readable data that can be extracted directly. These four schema types have the highest impact on AEO performance.

• 1.FAQPage schema is present on pages that answer questions - FAQPage is the highest-impact schema type for AI Overview inclusion. It creates an explicit question-answer mapping that AI retrieval systems can pull directly. The Q&A in the schema must match content that's visible on the page - don't use it for content that only appears in markup.
• 2.Article schema is present on all content pages - Article schema establishes the content type, author, publisher, and dates in a machine-readable format. It's a baseline requirement for AI engines to classify your page as authoritative content rather than an unknown document.
• 3.HowTo schema is used for step-by-step content - If a page describes a process with discrete steps, HowTo schema communicates that structure directly to AI engines. It increases the likelihood that AI responses cite your page for 'how to' queries.
• 4.Organization or Person schema is present sitewide - Organization schema on the homepage (or Person schema for individual creators) establishes the publisher identity that AI engines attach to every page on your site. Without it, your content is cited with less organizational context.

Category 6: Freshness Signals (Factors 22-23)

AI engines are calibrated to prefer current sources, especially for topics that evolve over time. Two factors control how AI engines assess the freshness of your content.

• 1.datePublished and dateModified are set in Article schema - These fields give AI engines an explicit, machine-readable freshness signal. A page with a dateModified from this year is scored as more current than a page with only a datePublished from two years ago, even if the content is identical.
• 2.In-prose freshness markers are present for time-sensitive claims - 'As of May 2026' and 'Updated for 2026' in the visible text reinforce freshness signals for AI engines that read prose. For fast-moving topics - AI tools, market statistics, product features - these markers signal that the content reflects current reality rather than a historical snapshot.

Category 7: Authority and EEAT (Factors 24-25)

Experience, Expertise, Authoritativeness, and Trustworthiness (EEAT) influence which content AI engines treat as reliable enough to cite. Two signals have the clearest impact.

• 1.A named author is present with a link to credentials or a bio - Anonymous content scores lower on EEAT than content with a named, verifiable author. An author bio page with credentials, a professional profile link, or a track record in the topic area increases trust signals for both AI engines and Google's quality raters.
• 2.An About page exists and describes the organization's expertise - AI engines evaluate publisher identity when assessing source reliability. An About page that describes who you are, what you do, and why you're qualified to write on your topics sends an explicit EEAT signal. It's a low-effort page that improves every piece of content on your site.

How to Use This Checklist

Run this SEO AEO checklist against your highest-priority pages first - the ones that target queries most likely to trigger AI Overviews or Perplexity responses. Not every factor will apply to every page. Category 5 (structured data) requires technical implementation; the others are content decisions you can make without touching code.

Prioritize by category: fix crawl access issues first (they block everything else), then structure, then facts, then entity clarity, then schema, then freshness, then EEAT. A page that passes all 25 factors isn't guaranteed to be cited - AI engines also weigh query relevance and competitive content - but it's built to compete for citations in a way that most content isn't.