Best Practices

Content patterns that improve AI visibility scores and anti-patterns to avoid.

Patterns That Improve Scores

These content patterns directly impact specific scoring criteria. The percentage in parentheses shows the criterion weight in the overall AEO Site Rank.

Q&A format headingsQ&A Format (5%)

Structure 50%+ of H2 headings as questions. AI engines match these directly to user queries. Use natural phrasing that mirrors how people ask questions.

FAQ sectionsFAQ (4%)

Include 3-5 Q&A pairs in a dedicated FAQ section with FAQ schema markup. Place at the end of the article before the conclusion.

Original dataOriginal Data (10%)

Cite real benchmark data, survey results, or proprietary statistics. AI engines prioritize sources with unique data over those that repeat common knowledge.

Direct answer paragraphsDirect Answer (5%)

Start key sections with a bold lead sentence (40-60 words) that directly answers the heading question. This is what AI engines extract for featured snippets.

Fact densityFact Density (6%)

Include named entities, specific numbers, and concrete examples. Sentences like "increased revenue by 34% in Q3 2025" score higher than vague claims.

Semantic HTML5Semantic HTML5 (2%)

Use proper heading hierarchy (H2 > H3, never skip levels). Structure content with ordered/unordered lists and definition lists where appropriate.

Internal linkingInternal Linking (4%)

Include 2-3 contextual cross-links to related content. Add a "Related Articles" section linking to 3-5 relevant pieces on the same domain.

Definition patternsDefinition Patterns (2%)

Include clear definitions for key terms, especially in introductions. Use the pattern "X is Y" or "X refers to Y" for maximum extractability.

Anti-Patterns to Avoid

Industry jargon without contextUsing specialized terms without explaining them reduces AI engine confidence in citing the content

Em-dashes and complex punctuationClean, simple punctuation parses better for AI extraction. Use spaced hyphens instead of em-dashes

Fabricated statisticsAI engines cross-reference data. Invented numbers damage credibility and can trigger trust penalties

Div soup / non-semantic markupGeneric div-based layouts instead of semantic HTML5 elements make content harder for AI engines to parse

Thin content sectionsH2 sections with less than 50 words signal padding rather than substance. Every section should add real value

Generic conclusionsConclusions that repeat "in conclusion" or summarize without adding insight. End with a specific actionable takeaway

Scoring Impact by Category

The scoring criteria are grouped into 5 pillars. Content creation in Studio primarily impacts the Answer Readiness and Content Structure pillars.

Answer Readiness~45%Topic coherence, original data, content depth, fact density, citation-ready writing, answer-first placement, evidence packaging

Content Structure~25%Content licensing, Q&A content, query alignment, FAQ, tables/lists, definitions, entity disambiguation

Trust & Authority~16%Entity authority, internal linking, freshness, schema.org, author schema

Technical Foundation~9%Semantic HTML, clean HTML, date signals, extraction friction, image context AI, schema coverage, speakable

AI Discovery~5%Cannibalization, llms.txt, robots.txt, velocity, licensing, sitemap, canonical, RSS

For a deep dive into all 53 criteria, see the Scoring System guide.

Content Quality Checklist

Run through this checklist before publishing any article from Studio.

50%+ of H2 headings are questions

3-5 FAQ pairs with FAQ schema

At least one original data point or statistic

Bold lead paragraphs (40-60 words) under question headings

Named entities and specific numbers throughout

Proper H2 > H3 hierarchy (no skipped levels)

2-3 internal cross-links in body text