Question Resolution Density

Question resolution density is the ratio of questions your content answers to questions the AI retrieval system asks. It is the practical optimization target for fan-out query coverage. A content gap in GEO is not a missing keyword but an unanswered question. A content refresh that adds prose without resolving a new question changes nothing. Pages that resolve zero of the questions the retrieval system generates do not participate in AI synthesis, regardless of domain authority or backlink profile.

How to Calculate Question Resolution Density

Start by identifying the fan-out queries an AI system would generate for your target topic. Ask ChatGPT, Perplexity, or Claude to list the sub-questions they would investigate for a given topic. Cross-reference with Google’s “People Also Ask” results. This produces an approximate question map of 10 to 30 sub-questions per topic.

Then audit your content against the map. For each question, determine whether your content provides a direct, extractable answer within the first 40 to 60 words of a section. Count the resolved questions and divide by the total questions on the map. A page resolving 18 out of 25 mapped questions has a 72% question resolution density.

Why Resolution Density Beats Word Count

Forum posts outrank authoritative guides. This happens precisely because forums resolve edge questions that no official guide covers. A Reddit thread answering “does [product] work with [obscure integration]?” fills a gap in the AI’s answer graph that a 5,000-word guide never addressed.
Adding words without adding answers is waste. Expanding a 2,000-word article to 4,000 words by adding context, history, and narrative does not improve question resolution density unless the new content directly answers previously unresolved sub-queries.
Competitors reveal your gaps. If a competitor is cited for a question your content does not answer, that question is a gap in your resolution density. The fix is not more content but a specific answer to that specific question.

Building a Question Map

For each target topic, build a question map using these sources:

AI platforms: Ask “What questions would you investigate to answer [topic]?” on ChatGPT, Claude, and Perplexity. Each generates slightly different sub-queries.
Google PAA: Collect all “People Also Ask” questions for your target keywords. These are Google’s own sub-query predictions.
Competitor content: Audit what questions competitor pages answer that yours do not. Each unanswered question is a resolution density gap.
Support and sales data: Real customer questions reveal sub-queries no AI tool can predict. FAQ pages built from actual customer questions resolve queries with high specificity.

For the complete content gap analysis framework, see the Generative Engine Optimization guide.