Aeo

Optimise content for LLM citation

AEO (Answer Engine Optimisation) optimises content to be cited in LLM-generated responses — distinct from SEO, which optimises for click-through rankings. The signal sets differ.

AEO vs SEO

	SEO	AEO
Optimises for	Click-through ranking	Citation as authoritative source
Success metric	Position 1-10, organic traffic	Citation count across LLMs
Key signals	Backlinks, keywords, page speed	E-E-A-T, structured facts, primary-source signals
Update cadence	Weeks–months	Days–weeks (LLM training cycles)

Both coexist — a page can rank #1 on Google AND be cited by Perplexity.

E-E-A-T signals for LLM citation

LLMs prefer content that looks like a primary source to their training data:

Signal	Implementation
Experience	First-person case studies; dated real examples ("In our 2026 audit of 50 repos…")
Expertise	Author bio with credentials; technical depth; cite primary sources
Authoritativeness	External backlinks from authority domains; schema.org markup; Wikipedia presence
Trustworthiness	HTTPS; contact info; transparent corrections; verifiable claims with data

Factual density — LLMs prefer pages with a high ratio of verifiable claims per 1,000 words. Prose that states facts > prose that describes opinions.

Content structure for citation

LLMs extract from:

1. Direct-answer paragraphs — a question as a heading followed immediately by a 1–2 sentence direct answer.
2. Structured data — FAQPage and HowTo schema (see schema-markup).
3. Definition blocks — "X is [concise definition]" sentences at the start of sections.
4. Comparison tables — LLMs reproduce tables well; use for vs. comparisons and feature matrices.

Citation-hostile patterns to avoid

• Gated content (LLMs can't read it during training crawls).
• JavaScript-rendered text (crawlers often miss it).
• Opinion without evidence ("we believe X is important").
• Content updated without a dateModified schema field.
• No author or institutional attribution.

Steps

1. Identify citation-worthy content. Pages that answer specific questions with unique data, first-party case studies, or authoritative comparisons are candidates. Brand-voice content without factual claims is not.
2. Audit E-E-A-T gaps. For each target page: score Experience (0–3), Expertise (0–3), Authoritativeness (0–3), Trustworthiness (0–3). Identify the lowest-scoring dimension and fix it first.
3. Add direct-answer structure. Rewrite the first paragraph of each target section as a direct answer to the implied question. Add schema (FAQPage/HowTo where appropriate).
4. Improve factual density. Replace opinion sentences with evidence sentences. Add specific numbers, dates, and attributable sources.
5. Track citations. Query each target LLM (ChatGPT, Perplexity, Claude) with the exact question the page answers. Note whether your domain is cited. Repeat monthly.
6. Verify. Rich Results Test for schema; check dateModified is present and accurate; confirm author bio is visible and crawlable.

Review checklist

• Direct-answer paragraph in each target section — question heading + immediate direct answer.
• E-E-A-T score ≥ 2/3 in all four dimensions — no dimension at 0.
• FAQPage or HowTo schema on pages with question-and-answer structure.
• Author bio visible and crawlable — not loaded via JavaScript after parse.
• dateModified in Article schema and visible on page.
• No gated content for citation-target pages — if it needs a login, it won't be cited.

Rules

• AEO applies only to pages with verifiable factual content — brand/opinion content is out of scope.
• Never fabricate statistics or case studies to improve E-E-A-T — citation by LLMs of false claims is a liability.
• Track citation rates monthly; treat uncited priority pages as an audit finding.