Why 'Readability Scores' Are Ruining Your AI Search Visibility
Simplifying text to a 6th-grade reading level destroys Lexical Diversity and BM25 matching. Technical, jargon-dense chunks perform better in RAG vector retrieval.
Score = 206.835 − 1.015 × (words/sentences) − 84.6 × (syllables/words)
AI Citation Rate by Flesch-Kincaid Reading Level
The Readability Paradox
Source: RankAsAnswer readability analysis across 8,400 audited pages · 2025
The readability trap
For 15 years, content marketers were told to write at a 6th-grade reading level. Tools like Hemingway App, Yoast SEO's readability checker, and Grammarly's clarity scores trained an entire generation of writers to simplify, shorten, and dumb down their content. Short sentences. Active voice. No jargon. Bullet points over paragraphs.
This advice optimized for human reader engagement, email newsletter open rates, and traditional SEO metrics. It is actively counterproductive for AI search visibility. The same simplifications that make content easier for humans to skim make it worse for vector retrieval — and significantly worse for BM25 keyword matching in hybrid search systems.
The counterintuitive finding
Lexical diversity and BM25 matching
Lexical diversity — measured as the ratio of unique vocabulary tokens to total tokens in a text — is a core driver of retrieval performance in both traditional BM25 and modern vector search.
High-readability content achieves its simplicity by using fewer, more common words repeatedly. "Use" instead of "leverage," "employ," "utilize," or "implement." "Good" instead of "effective," "robust," "high-fidelity," or "optimized." This vocabulary reduction collapses your lexical diversity score and reduces the number of unique BM25 match opportunities.
TTR = Type-Token Ratio: unique word types divided by total word tokens. Higher is better for retrieval.
The Flesch-Kincaid problem: what the score actually measures
The Flesch-Kincaid readability formula measures two things: average sentence length and average syllables per word. A lower score (easier reading) is achieved by writing shorter sentences with shorter words.
But in RAG retrieval, shorter words and shorter sentences are penalized twice:
Penalty 1: Syllable reduction kills technical vocabulary
Multi-syllable technical terms — 'hyperparameter,' 'tokenization,' 'embedding,' 'cosine similarity' — are the exact terms that match technical user queries. Replacing them with 'setting,' 'processing,' 'conversion,' and 'similarity' loses those specific match opportunities.
Penalty 2: Sentence length reduction destroys entity density
Short sentences carry fewer entities. 'Use our tool to improve your site' has 2 entities. 'RankAsAnswer's 28-signal AEO audit identifies schema gaps, heading hierarchy failures, and E-E-A-T deficiencies that prevent AI citation' has 7 entities in one sentence.
Why technical jargon wins RAG vector retrieval
Technical jargon performs well in RAG for three compounding reasons:
Query intent alignment
Technical users — who represent the majority of AI search power users — write technical queries. 'What is RAG retrieval optimization?' returns far more specific results than 'how to improve AI search.' Your jargon-dense content matches jargon-dense queries.
Vector space specificity
Technical terminology creates embeddings in specific, narrow regions of vector space rather than crowded general-business clusters. A page about 'BM25 lexical matching in hybrid retrieval systems' occupies a nearly unique vector position. A page about 'improving how AI finds your content' is in a cluster with millions of other pages.
Expertise signaling
LLMs have learned to associate technical vocabulary with authoritative sources during training. A page that uses correct technical terminology for a field produces higher confidence in the model's citation decision than a simplified page on the same topic.
When readability still matters (and when it doesn't)
The optimal content formula: technical depth + structural clarity
The goal is not to write incomprehensibly dense prose. It's to combine technical vocabulary (high lexical diversity) with clear structural organization (headings, lists, tables). This combination captures both AI retrieval performance and human comprehension:
- ▸Use technical terms precisely — but define them on first use for accessibility
- ▸Allow longer sentences in technical sections where entity density is high
- ▸Use structured elements (tables, lists, headings) to maintain scannability despite technical density
- ▸Reserve simplified language for introductions and summaries that serve human readers entering your content