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AEO Content Scorer

What it does

The AEO Content Scorer (Answer Engine Optimization) analyses content against the structural patterns that AI search engines — ChatGPT, Claude, Perplexity, Google AI Overviews — favour when extracting answers from web pages. It scores on atomic paragraph length, definitive vs hedge-laden tone, presence of explicit question-and-answer structure, list density, citations and named entities, sentence clarity, and heading frequency. The output is a per-dimension score with a rolled-up overall, plus prioritised recommendations.

Common situations

You have a piece of content that ranks well in classic Google search but never gets cited in ChatGPT or Perplexity answers. The AEO scorer tells you whether the content is structured the way LLMs prefer to extract from — short, atomic, definitive, list-friendly. Most content optimised for traditional SEO is the wrong shape for AI extraction; the rewrite path is structural.

You are writing a new pillar page and want to make sure it is shaped for both Google ranking and AI citation. Run the draft through the scorer; tighten paragraphs, add explicit questions, surface citations, and the same content becomes more eligible for both forms of discovery.

A FAQ page or a knowledge-base article should be a natural fit for AI extraction but isn’t getting cited. The scorer reveals what’s missing — usually the question structure isn’t explicit enough (questions are implied rather than stated as headings) or the answers are buried in long paragraphs instead of leading the section.

You are auditing a competitor’s content that is being cited in AI answers more often than yours. Paste their content; see the structural patterns. AI engines reward specific shapes — atomic paragraphs, explicit Q&A, lists, citations — and the gap is usually visible in the scorer’s per-dimension scores.

A piece of content has been written by an AI tool and the output reads professional but is mediocre on every AEO dimension. AI content tends to be hedge-heavy (“might be”, “could be”, “is often”) and paragraph-long. The scorer surfaces it; the edit cuts hedges and breaks paragraphs.

What you need to know

Answer Engine Optimization (AEO) — also called Generative Engine Optimization (GEO) — is the practice of structuring content so that AI search systems can extract and cite it accurately. The 2024-2026 shift in search behaviour has made this a real discipline: Google AI Overviews, Bing Copilot, Perplexity, and direct LLM queries (ChatGPT, Claude) now answer many queries without sending the user to a website. Sites cited in those answers get the brand exposure; sites that aren’t, don’t.

The scorer measures seven dimensions, each chosen because LLM extraction patterns visibly prefer them:

Atomic paragraphs (1-4 sentences each). LLMs extract better from short paragraphs because each one is a self-contained unit. Long paragraphs require the model to summarise, which loses fidelity. Pages where most paragraphs are 1-4 sentences score high; pages with 6-10-sentence paragraphs score low.

Sentence clarity (average length under 20 words). Short, direct sentences are easier to lift verbatim. Long, comma-laden sentences require the model to rephrase, increasing the chance of misrepresentation or omission.

Definitive tone (low hedge-word density). LLMs prefer claims they can present as fact. Content riddled with “might”, “perhaps”, “could be”, “in some cases” forces the LLM to qualify the claim, which often results in the claim being skipped entirely. Definitive content gets cited; hedged content gets ignored.

Q&A signal (explicit questions in the content). When a page poses a question explicitly (“What is X?”) and answers it directly afterwards, LLMs lift the answer with high confidence. Implicit questions (“Many people wonder about X — here’s what we think”) don’t get the same treatment.

List structure (bulleted or numbered lists). Lists are the highest-confidence extraction format for LLMs — they map cleanly to bulleted answers in chat outputs. A page with several lists scores high; a wall-of-prose page scores low.

Citations and entities (links to sources, named proper nouns). LLMs use citation density and entity references as proof signals. Pages with inline links to authoritative sources, named entities (people, companies, places, products), and dated references rank higher in extraction confidence.

Heading density (subheadings every ~150-250 words). Headings are landing points for LLM attention. Pages with frequent, descriptive subheadings get sectioned extraction; pages without headings get holistic summarisation, which is lower-fidelity.

The score is the average of the seven dimensions. Anything above 80 is strongly extractable. Below 60 means the content needs structural rework before AI engines will reliably cite it.

This is a heuristic-based check, not an LLM call. The scorer uses pattern detection on the text — no API calls, no usage costs, runs entirely in the browser. The trade-off: heuristics can’t measure semantic quality, only structural patterns. Content that is structurally optimal but factually wrong scores well; content that is brilliant but unstructured scores low. Treat the score as a pre-flight structural check, not as a full quality assessment.

Frequently asked questions

What’s the difference between AEO and SEO?

AEO is a subset of SEO focused on extraction by AI search engines rather than ranking on classic SERPs. The two overlap heavily — well-structured content tends to do well in both — but the optimisation patterns differ. SEO favours topical depth and keyword presence; AEO favours atomic structure and definitive claims.

Are AEO and GEO the same thing?

Effectively yes. “AEO” (Answer Engine Optimization) and “GEO” (Generative Engine Optimization) are competing names for the same discipline. AEO is slightly more common in technical SEO circles; GEO is slightly more common in consultancy marketing. Use whichever your audience uses.

Will AEO replace classic SEO?

No — augment, not replace. Classic SEO continues to drive most search traffic; AEO drives an increasing share of brand-mention exposure as AI search penetrates. Optimising for both is the right answer. Most AEO improvements (clearer structure, better citations, more lists, atomic paragraphs) help classic SEO too.

How is the score calculated?

Each of the seven dimensions is scored 0-100 against pattern-detection heuristics. The overall score is the average. The per-dimension scores tell you what to fix; the overall tells you whether the content has structural issues.

Why doesn’t the scorer use an actual LLM?

LLM API calls cost money. The scorer is free, runs in the browser, and can be used unlimited times. Heuristic-based scoring is necessarily approximate but the dimensions chosen correlate well with observed AI extraction behaviour. The scorer is calibration, not certification.

What hedge words count as hedges?

Common ones: “might”, “may”, “could”, “perhaps”, “possibly”, “arguably”, “kind of”, “sort of”, “tends to”, “often”, “usually”, “in some cases”, “I think”, “I believe”, “we feel”. The scorer’s hedge list is a fixed set; legitimate qualifications can read as hedges if you write cautiously.

Should I avoid hedges entirely?

No — sometimes hedging is honest and accurate. The scorer flags excessive hedge density, not all hedging. A piece of content with one hedge per page reads as careful; with one hedge per paragraph reads as evasive.

Does the scorer support languages other than English?

Partially. The structural patterns (paragraph length, list density, headings) work for any language. The hedge-word and stop-word lists are English-only. For non-English content, structural scores are accurate but tone scores are approximate.

Common problems

Problem: Score is high but the content still isn’t being cited in AI answers.

Structure is necessary but not sufficient. AI engines also weight authority, freshness, factual accuracy, and topical relevance. A structurally clean page about an obscure topic still won’t be cited because the topic doesn’t generate AI queries; a structurally clean page on a hot topic might still lose to higher-authority sites.

Problem: Atomic paragraph score is low but the content reads well.

Long paragraphs that read well to humans can still be the wrong shape for LLM extraction. Consider whether breaking them at sentence boundaries hurts readability or improves it. Often the long paragraphs can be split with no readability loss; sometimes they need to stay long because the argument is genuinely cumulative.

Problem: Definitive tone score is low and the content is on a topic where confident claims are inappropriate.

Some topics genuinely require hedging — medical advice, legal opinions, financial recommendations. The scorer doesn’t know the topic. Accept the lower tone score; the trade-off is between AI citability and topical responsibility.

Problem: Citations score is low but the content is original analysis.

Citations are proof signals to LLMs. Even original analysis benefits from citing sources for the underlying data. Add inline links to relevant authoritative pages where claims need backing.

Problem: List structure score is low but the topic doesn’t lend itself to lists.

Not everything is a list. Topics that flow as narrative prose are harder to score on the list dimension — accept the lower score and rely on the other dimensions. The score is per-dimension specifically so you can see where structure helps and where it doesn’t fit.

Tips

  • Lead each section with the answer, then expand. LLMs lift the lead sentence of a section more than the conclusion.
  • Use explicit questions as subheadings (“What does AEO mean?”) followed by direct one-paragraph answers. Highest-confidence extraction format.
  • Cite specific sources inline. “According to the 2024 W3C accessibility guidelines, …” with a link is much stronger than “Industry standards say …” without one.
  • Break paragraphs at sentence boundaries when the sentences are independent. Long paragraphs are an SEO inheritance from print writing; LLMs prefer short ones.
  • Hedge only when honest. “We don’t know” is fine; “in many cases probably this could be true” is hedge spam.
  • Run the scorer at draft stage, not just at publish. Structural fixes are cheap pre-publish; harder once the content is live.

Related tools in this suite

The Word Count and Keyword Density Checker cover length and topical signal — complementary lenses on the same content. The Heading Outline Checker verifies heading structure, which feeds the heading-density dimension of the AEO score.

What this looks like at scale

For a single page, the scorer is fine. For a content set, AEO becomes part of editorial discipline — paragraph length checked at edit time, explicit Q&A structure encouraged for FAQ-style content, citations required for claim-heavy articles. The structural patterns the scorer measures are also good editorial patterns; making them part of the writing culture is more sustainable than fixing them after publish.

Take it further

If a content set’s AI search visibility is meaningfully behind classic search performance, the right scope is a content-architecture pass — restructuring representative pillar pages to AEO patterns, then templating the patterns into the editorial workflow. Start a conversation about what that looks like at the size of your content set.