How to Calculate Your Own Information Density Score

What is information density?

Information density is the ratio of "factual content tokens" to total tokens in a piece of text. In the context of AI search and RAG retrieval, high-density content contains many specific, verifiable, citable facts relative to its total word count. Low-density content is padded with filler text, generic observations, and qualifying language that contains no new information.

LLMs are optimized for information extraction. When they retrieve a chunk, they're looking for the specific facts they can use to build an answer. Chunks with high information density yield more usable facts per context window token — making them both more likely to be retrieved and more likely to be cited.

The information density formula

The practical information density formula counts three types of high-signal tokens:

Information Density Score

(P + N + D) ÷ W × 100

P= Proper Nouns (brand names, people, places, products) N= Numbers (statistics, percentages, prices, counts) D= Dates (specific dates, years, time references) W= Total word count of the passage

A score of 15 means 15% of your words are high-signal fact carriers. A score of 5 means 95% of your content is filler or generic text.

Manual calculation walkthrough

Let's calculate the information density of two real paragraph examples to illustrate the difference:

Example A: Low density paragraph

"Content marketing is really important for businesses today. Many companies are investing in creating content that helps their customers learn about their products and services. When you create good content, you can attract more visitors to your website and potentially convert them into customers."

Word count (W): 52 words

Proper nouns (P): 0

Numbers (N): 0

Dates (D): 0

Information Density Score: (0 + 0 + 0) ÷ 52 × 100 = 0%

Example B: High density paragraph

"B2B content marketing generated a median of 3.2x more pipeline per dollar than paid advertising in 2025, according to Demand Gen Report's annual benchmark survey. Companies using RankAsAnswer's AEO-optimized content framework saw 41% citation rate improvements within 90 days across ChatGPT, Perplexity, and Google AI Overviews."

Word count (W): 48 words

Proper nouns (P): 6 (B2B, Demand Gen Report, RankAsAnswer, ChatGPT, Perplexity, Google AI Overviews)

Numbers (N): 4 (3.2x, 41%, 90 days)

Dates (D): 1 (2025)

Information Density Score: (6 + 4 + 1) ÷ 48 × 100 = 22.9%

Industry benchmarks by content type

Content type Target density score Minimum for AI citation

Improving your information density score

Automating information density analysis at scale

Manually calculating information density for 50+ pages is impractical. RankAsAnswer's page analyzer automatically calculates information density scores across your entire domain, flagging the pages with the lowest scores and generating specific recommendations for which passages to improve.

The density report shows you your site's average score, your worst-performing pages, and a comparison against the highest-citing competitor pages in your category — so you have a clear gap analysis to prioritize your content improvement work.

Why readability scores hurt AI visibility The full argument for why 6th-grade writing standards are hurting your RAG performance. Write content AI models want to cite The comprehensive writing framework for RAG-optimized content creation.