Writing content AI engines can quote

## The mistake almost everyone makes

When teams hear "optimise content for AI", they typically rewrite for clarity and call it done. That's not enough. AI engines have a very specific structural preference: they lift sections of content **verbatim** into their answers, and they prefer content that's already shaped for verbatim quotation.

This is the difference between content the AI can *understand* and content the AI can *use*. Plenty of well-written marketing copy clears the first bar and fails the second. Answerability is the second bar — and it's the one that decides whether your page becomes a citation source or just another link the model glanced past.

## The structural rules that work

**Questions as section headings.** Phrase your H2s as the question a buyer would actually type. "How does carbon offsetting work?" is quotable. "Carbon Offsetting: An Overview" is not. The AI is pattern-matching headings against incoming queries, and only the question-phrased ones match. A page with 6 question-phrased H2s typically gets 3-4x the citation rate of an identical-content page with descriptive H2s.

**Short, declarative first sentences.** AI engines lift the first sentence of a section into their answer most often. Make it land. Lead with the answer, not the setup. "Pricing starts at £29/month for the Starter plan" beats "We offer flexible pricing designed to scale with your team" every time, even though the second sentence "sounds better" in isolation.

**Concrete numbers, dates, comparisons.** "FAQ schema pages get cited 3x more often than prose pages" is quotable. "FAQ schema is important" is not. AI engines disproportionately surface content with specific, verifiable claims — partly because verifiability is itself a trust signal, partly because numbers compress cleanly into answer formats.

**Lists with bolded keys.** Bullet points with a bolded term at the start of each item compress beautifully into AI-generated answers. The model can lift the bolded keys as a structured rundown. A four-item bolded list is one of the most-quoted formats in our citation logs — far more than equivalent prose.

**Schema markup as belt-and-braces.** Even if your prose is well-structured, AI engines also parse JSON-LD schema. A FAQPage block on your pricing page is the single highest-impact GEO win for most B2B sites. Pair the schema with visible H2 questions and the page wins on both extraction paths simultaneously.

**Tables for comparison content.** When you're comparing pricing tiers, feature sets, or product specs, a structured HTML `<table>` (not an image of a table) lets the AI lift the comparison verbatim. Comparison queries are some of the highest-intent traffic; getting your table cited is a leveraged win.

## The wrong patterns to avoid

**Walls of prose without subheadings.** Even strong writing becomes invisible to AI if there's no structure to lift from. We see 2,000-word essays that read beautifully and earn approximately zero AI citations because there's nothing for the model to anchor on.

**Vague subheadings.** "Our Approach", "The Difference", "About Us" — these mean nothing to the model. It can't pattern-match them to a buyer query. Every vague H2 is a missed extraction opportunity.

**Styling-only "headings".** Bold-uppercase paragraphs that LOOK like headings but aren't real `<h2>` / `<h3>` tags get ignored. Use real markup. Many Webflow and Framer templates default to this anti-pattern; double-check your CMS output.

**Long preambles.** The AI usually reads the first ~200 words of a section. If you spend that on backstory, the operational content gets cut. A common failure mode: "In today's fast-moving market, businesses need to…" — three sentences of throat-clearing that crowd out the answer.

**Images carrying critical content.** Pricing in a screenshot, key data in an infographic, the answer hidden inside a hero image. AI engines can OCR but they don't trust it the way they trust real text. If a number matters, put it in real text.

**Single-paragraph FAQ sections.** "FAQ" as a heading with eight questions inside a single prose block. The AI can extract questions from real FAQPage schema or from question-phrased H3s; it struggles with prose FAQ sections.

## A worked example

A SaaS pricing page rewritten for answerability moves from:

> "Our plans are designed to scale with you, whether you're a small team just getting started or an enterprise managing thousands of accounts."

to:

> "## How much does {product} cost? — Pricing starts at £29/month for the Starter plan, £99/month for Pro, and £299/month for Business, with annual billing saving 20%. All plans include unlimited users and a 14-day free trial."

The second version gets lifted verbatim into "How much does {product} cost?" queries and ranked highly on "is {product} expensive" and "{product} pricing tiers" queries. The first version gets ignored because there's nothing extractable — no numbers, no specific tier names, no concrete claim the AI can quote with confidence.

Same page, same intent, dramatically different citation outcome.

## The pages that matter most

Not all pages are worth rewriting. The leverage curve is steep — your top 5-10 pages typically account for 70-80% of citable surface. Prioritise in this order:

**1. Homepage.** Answers "what is {brand}?" and is referenced by basically every direct-recall query. Highest single-page leverage. Worth rewriting first.

**2. Pricing page.** Comparison queries hit this constantly. AI engines love verbatim pricing data, and a well-structured pricing page becomes the canonical reference even for queries that don't explicitly ask about cost.

**3. Product / feature pages.** One per major capability. Each answers a specific buyer query class — "does {brand} do X?", "how does {brand} handle Y?". Schema and question-phrased H2s compound hardest here.

**4. Comparison pages.** "X vs Y" pages, if you have them. High intent, high citation rate, often easy to make answerable because the structure is naturally comparative.

**5. FAQ or help-centre top pages.** Already question-shaped; usually just need schema and slight rewriting to land cleanly.

Pages below the top 10 rarely move the curve enough to justify the editorial effort. Spend the editorial budget on placement supply instead — covered in our lessons on PR and authority signals.

## How Rankply finds the gaps for you

The platform-scan component crawls every page on your site and scores it against these answerability rules. Each page gets a per-rule breakdown: heading quality, first-sentence extractability, list structure, schema coverage, claim density. Low scores get flagged in the recommendations panel with specific edits to ship.

You can act on them yourself or hand them to our editorial team as part of your monthly delivery — the calendar shows which pages are queued for rewrite, when they'll ship, and the answerability lift we expect from each. The recommendations panel ranks fixes by expected citation lift, so even if you only have time for the top three, you're hitting the ones that matter.

Most customers complete their first answerability pass — top 10 pages — in 4-6 weeks. After that, the citation rate on those pages typically lifts 25-50% within 60 days, and the higher rate persists as long as the content stays relevant. It's the closest thing GEO has to a one-time fix with permanent payoff.