10 Content Quality Dimensions

Does your content answer what users are really looking for? AI does not cite pages that miss the query intent - even if they contain the keyword. CSI Alignment measures how precisely your article matches the expected answer type: definition, comparison, instruction, or recommendation.

How many facts, data points and concrete statements a single paragraph contains. Low-density content is filler - AI skips it. This dimension rewards every sentence that adds something new and penalizes text that repeats the same idea in different words.

AI models prefer content that delivers the main answer in the first paragraph. BLUF measures how quickly your content gets to the point.

Content builds a network of related concepts: entities, attributes and values. The denser and more coherent the graph, the easier it is for AI to recognize the content as a credible source in the domain. This dimension checks whether your texts create knowledge - not just mention it.

AI "extracts" self-contained fragments from content - sentences or paragraphs that can be understood without the full article context. This dimension measures how many such citation-ready units your page contains. The more distinct chunks, the higher the chance of appearing in AI Overview or an assistant answer.

How much effort the model has to spend to pull a concrete answer from your content. Long sentences, redundant intros and unnecessary disclaimers raise the cost of retrieval. A low score here means AI will pick a competitor who said the same thing in fewer words.

Does your content add something the other 10 pages in the top 10 do not have? Information Gain measures the difference between what you write and what is already widely available. This metric is boosted by unique data, original observations and non-obvious conclusions.

A single user query generates dozens of related sub-questions that AI resolves in the background. This dimension checks how many of those satellite questions your content answers. Wider topic coverage = higher probability that a fragment of your page appears in the synthetic answer.

Semantic Role Logic evaluates whether the content has a clear structure: subject, action, effect. The more precise the language, the easier it is for the model to cut out and cite exactly the fragment that fits the answer.

A classic statistical metric comparing word frequency in your text to norms for the given query in the top 10 SERP. Too low - the topic is treated superficially. Too high - a keyword-stuffing signal. CitationOne points to exactly which terms are underrepresented compared to competitors.