Content Strategy for AI

Use question-format headings (H2/H3) that match how people query AI assistants. Start each section with a direct answer in 1–2 sentences, then add supporting detail and data. Use bullet lists for enumerable items. This inverted-pyramid style makes it easy for AI systems to extract and cite your answer.

At least 20–30 substantive questions across your key topics. Quality over quantity — each answer needs 3–5 sentences minimum and genuine depth. Group FAQs by category with clear headings. Include FAQPage JSON-LD schema so AI engines parse the Q&A structure directly. Our FAQ has 160 items across 15 categories — that's the kind of coverage that gets cited.

Proprietary data, first-hand research, expert analysis with author credentials, unique case studies, perspectives you won't find elsewhere. AI engines value original sources because they add new information to the knowledge base. Rephrasing content from other sources adds zero value for AI citation.

Create content for humans first, then structure it for AI extraction. The best AEO content is genuinely useful to readers AND easy for AI to parse. That means clear headings, direct answers, proper schema, and author attribution. Don't create low-quality keyword-stuffed content — AI engines are smart enough to spot it.

Very. AI systems use author info as an authority signal — especially for YMYL topics like health, finance, and legal content. Include names, credentials, professional background, and links to profiles. Add Person schema markup. Anonymous content is at a serious disadvantage for AI citation.

Both — but updating existing high-performing content often yields faster results. Add Q&A headings, FAQ schema, author attribution, and internal links to existing pages. For new content, focus on topics where you have genuine expertise or original data. AI engines value freshness, so update publication dates when you substantially revise content.

Focus on the universal AEO criteria: structured data, clean HTML, entity clarity, Q&A formatting. These work across all engines. Multi-engine audits reveal engine-specific gaps — Claude weights structured data heavily while ChatGPT prioritizes conversational relevance. Address the common foundation first, then fine-tune for specific engines.

There's no magic number, but content must fully answer the question. Under 300 words rarely gets cited — too thin. Over 5,000 words can bury key answers. The sweet spot is thorough coverage with clear structure. Use headings to make long content navigable. Put answers in the first paragraph of each section.

AI engines prefer recently updated content, especially for time-sensitive queries. ChatGPT inherits Bing recency signals - content updated within 90 days gets a retrieval boost. Add dateModified to your Article schema, update publication dates on substantially revised pages, and maintain a regular publishing cadence to signal ongoing relevance.

Group questions by topic category with clear H2 headings per category. Each question should be an H3 with the answer immediately below. Lead every answer with a direct 1-2 sentence response, then elaborate. Add FAQPage JSON-LD schema matching every visible Q&A pair. Avoid hiding answers behind JavaScript accordions - AI crawlers need the HTML visible in the source.

Topic coherence measures how tightly your blog content clusters around related themes. It is still the single heaviest criterion in the framework. The scorer analyzes sampled pages, building term frequency maps and bigram clusters to detect thematic focus. Sites with scattered topics (AI, recipes, fitness) score low. When coherence drops below 6/10, a "coherence gate" caps your entire AEO Site Rank - no matter how perfect your technical implementation.

The coherence gate is a unique scoring mechanic: when your topic coherence score drops below 6/10, your entire AEO Site Rank gets capped at 35 + (coherence_score * 5). A coherence score of 3 means your maximum possible AEO Site Rank is 50, regardless of how well you score on every other criterion. Fix this by removing off-topic blog content and building intentional topic clusters around your core themes.

Content Depth measures average word count, heading structure, and the ratio of substantive pages to thin pages. Pages under 500 words rarely get cited by AI. The sweet spot is 1,500-3,000 words with 5+ H2 headings. Content Depth is cross-linked with Topic Coherence - deep content on scattered topics gets penalized because depth without focus doesn't build authority.

Query-Answer Alignment measures how well your content's headings and phrasing match the natural language questions users ask AI engines. Generic headings like "Overview" or "Features" don't match any real queries. Rewrite them as questions users actually ask: "How Much Does Live Chat Cost?" or "How Do I Set Up AI Support?" Use your visibility report's MISS queries as a roadmap.

Content cannibalization happens when multiple pages on your site target the same topic, forcing AI to guess which one to cite. The scorer compares page titles and content similarity across your site. Fix it by consolidating competing pages into one authoritative resource with 301 redirects from the old URLs. When Topic Coherence is low, the cannibalization penalty becomes more aggressive.

Visible Date Signal checks whether your pages display clear publication and update dates. AI engines have a recency bias - content without visible dates gets treated as potentially stale, even if it was published recently. Add both "Published" and "Last Updated" dates to your article template, and match them with datePublished and dateModified in your Article schema.

Direct Answer Density (5% weight) measures how consistently your content leads with the answer right after each heading. AI doesn't read full articles - it scans headings and grabs the first 1-2 sentences. If those sentences contain filler ("In today's fast-paced world..."), AI moves on. Lead every section with a concrete fact or answer, then elaborate. This applies to all headings, not just questions.

Author & Expert Schema (3% weight) proves that a real, credentialed person wrote your content. Person schema with name, jobTitle, sameAs links to LinkedIn, and knowsAbout fields lets AI cross-reference the author's identity. Publishing as "Admin" or "Staff Writer" is a negative trust signal. Author schema compounds with Entity Authority and Original Data to form the trust foundation AI uses for citation decisions.