Fact Blocks: The Content Pattern Claude Cites First

Here's what Claude actually extracts from your pages - and it's not paragraphs.

Claude's citation mechanism works by pulling discrete factual units from source content, incorporating them into responses with attribution. The format of these units directly influences extraction confidence and citation frequency. Content structured as "fact blocks" - specific patterns of claim, evidence, context - matches Claude's internal reasoning and gets preferentially selected.

A fact block has three parts in sequence. First: a named claim or statistic - specific, verifiable, with a number, date, or categorical assertion. Second: a source or evidence marker - where the claim comes from (company data, industry report, user survey, independent audit). Third: contextual framing - why the number matters or how it compares to alternatives.

We've tracked what we call "fact density" - extractable fact blocks per page. A page with 10 well-structured fact blocks offers 10 citation opportunities. A page with 2,000 words of narrative and 1 citable fact? Minimal citation material. Claude doesn't penalize prose-heavy content, but it preferentially extracts from pages where facts are clearly separated from surrounding narrative.

Claude also grades verifiability. Sourced claims ("according to Gartner's 2025 Market Guide") get higher extraction confidence than unsourced ones ("the market is growing rapidly"). Specific numbers ("47% increase year-over-year") beat vague quantifications ("significant growth"). Temporal markers ("as of Q3 2025") beat undated assertions because Claude can assess recency.

Definition-then-evidence sequences are another high-value pattern. Define a term in sentence one, provide evidence in sentence two, and Claude can extract the definition as a standalone factual unit. That pattern is gold for "What is X?" queries.

ChatGPT's citation approach is more fluid. It synthesizes across multiple passages and paragraphs, building composite answers from scattered facts. It doesn't need content in discrete fact blocks - ChatGPT is effective at extracting from narrative prose, conversational explanations, even informal blog posts. Content structure has less impact on whether ChatGPT cites it.

The culprit behind the difference: architectural priorities. ChatGPT is optimized for natural, conversational synthesis. Claude is optimized for accurate, attributable responses verifiable against sources. Claude's accuracy emphasis makes it more selective - it prefers content already structured to minimize misrepresentation risk during extraction.

Google AI Overviews generates short summaries with source links from its search index. Citation is driven by search relevance and page authority, not content structure. Google's featured snippets and knowledge panels do favor structured factual content - partial alignment with Claude - but the primary driver is traditional SEO.

Perplexity extracts and cites with explicit source links, similar to Claude's attribution approach. But Perplexity's extraction is driven by retrieval relevance (passage-query match) rather than structural formatting. It'll cite a passage from deep in a long article if it matches the query, regardless of fact block formatting.

The bottom line: optimizing for Claude fact block extraction delivers the largest marginal return on Claude specifically. ChatGPT will cite your content anyway (if relevant and authoritative). Perplexity cites if it retrieves. Google cites based on ranking. But Claude's preferential extraction of structured fact blocks means reformatting existing content can unlock significantly higher Claude citation rates - without touching your other engine scores.

LiveChat.com demonstrated the highest fact density in our cohort. Product pages and blog content consistently used claim-evidence-context patterns. Statements structured as extractable units with specific numbers - response times, integration counts, uptime percentages. Feature comparisons formatted as discrete facts with hard data. Fact density: 4-6 blocks per page. Claude bonus: +12. Claude had abundant, well-structured material to cite.

Tidio.com used a variation: definition-then-evidence sequences. Product docs opened with clear definitions ("Tidio is an AI-powered customer service platform that combines live chat, chatbots, and email marketing") followed immediately by evidence ("used by over 300,000 websites globally"). Blog content included structured comparison data blocks Claude could extract as standalone units. Fact density: 3-5 per page. Claude bonus: +14.

HelpSquad.com is the contrast case. Content was primarily narrative and sales-oriented: "Our team of trained professionals provides 24/7 live chat support to help your business grow." That sentence has a factual claim (24/7 support) but no specific evidence, no source attribution, no comparative context. Claude could extract "24/7 live chat support" but with low confidence - it reads as marketing copy, not verifiable fact. Fact density: approximately 0.5 blocks per page. Claude penalty: -5.

Crisp.chat (overall: 34) had moderate fact density on technical docs. Specific integration counts, API response time benchmarks, feature specifications structured as extractable facts. Claude gave Crisp +17 partly because within the content they did publish, fact density was reasonable. That's a key finding: fact density is evaluated relative to content volume. A small site with concentrated facts can score proportionally well.

LiveHelpNow.net (ChatGPT: 52) has moderate fact density with some well-structured feature descriptions but fewer comparison data blocks. Content tends toward feature lists without contextual framing - extraction material without the context component that increases citation confidence.

Start here: audit existing content for fact density. For each page, count discrete, extractable claims with specific numbers, named sources, or verifiable assertions. Pages under 2 fact blocks per 500 words need restructuring. The goal isn't adding filler statistics - it's reformulating existing claims into the claim-evidence-context pattern Claude preferentially extracts.

Restructure key claims into the three-part format. Take your most critical assertions: (1) Specific claim with a number - "Our platform integrates with 200+ tools"; (2) Evidence marker - "(verified integration directory, January 2026)"; (3) Contextual framing - "making it one of the most connected customer support platforms in the mid-market segment." Same information, dramatically more extractable.

Create comparison data blocks for competitive content. Structure each comparison point as a discrete unit: "[Your product] processes requests in an average of 1.2 seconds (internal benchmark, Q4 2025), compared to the industry average of 3.8 seconds (Gartner, 2025)." These are high-value citation material - users constantly ask Claude to compare products and services.

Open key pages with definition-then-evidence sequences. First sentence: clear definition. Second sentence: supporting evidence. "AEO (AI Engine Optimization) is the practice of optimizing website content for visibility in AI assistant responses. Studies show that 64% of users now start product research by asking AI assistants rather than using traditional search engines (Salesforce, 2025)." These opening sequences are the most frequently extracted content because they match "What is X?" query patterns.

Add temporal markers and source attributions to every factual claim. Every statistic needs a date ("as of February 2026," "based on 2025 data") and a source ("company records," "industry analyst report," "independent audit"). Undated, unsourced claims reduce extraction confidence - Claude can't assess recency or verify. Even internal data should be attributed: "based on analysis of 10,000 customer interactions (internal data, January 2026)" beats "based on our experience with thousands of customers" every time.