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When ChatGPT answers a question, it selects content based on structural alignment — not keyword density. Content Retrieval shows you exactly what that alignment looks like for any prompt you track: what elements AI consistently picks up, how your own content compares at the chunk level, and which claims your competitors have locked down. The result is a concrete, page-checkable list of changes you can make before the next response cycle runs.

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Content requirements

Content requirements are the structured output of GenRank’s analysis of real ChatGPT responses for a given prompt. Rather than showing you what AI said, they translate those responses into a checklist your content team can act on. GenRank analyzes a batch of real LLM responses for a prompt and extracts recurring patterns across them:
  • Answer formats — does AI answer with a numbered list, a comparison table, a definition-first paragraph, a step-by-step guide?
  • Claims — which factual assertions appear repeatedly, regardless of the source?
  • Definitions — which concepts does AI consistently define, and how?
  • Comparisons — which alternatives or competitors does AI routinely place alongside your brand?
  • Attributes — what properties, features, or qualifiers does AI associate with solutions in this category?
  • Supporting elements — what kinds of evidence (statistics, use cases, testimonials) does AI draw on to substantiate its answers?
Each of these patterns becomes a page-checkable requirement. If AI consistently includes a specific type of definition when answering a prompt, that’s not coincidence — it’s a retrieval signal. Your page either satisfies it or it doesn’t.
Content requirements update when GenRank re-analyzes response batches. Revisit them periodically, especially after major competitor content changes or shifts in your prompt’s mention rate.

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Vector similarity analysis

Content requirements tell you what AI looks for. Vector similarity analysis tells you whether your content delivers it — and exactly where it falls short. GenRank simulates how LLMs embed your content and compares those embeddings against the real prompts you track. This analysis runs at the chunk level, meaning your page is broken into sections (typically by heading or paragraph block) and each chunk is scored independently for semantic alignment with the prompt.

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What you see

For each URL you analyze, you get a visual breakdown of chunk-level similarity scores:
  • High-alignment chunks — sections that closely match the prompt’s embedding space. These are sections AI is likely to retrieve when generating a response.
  • Low-alignment chunks — sections that are semantically distant from the prompt. These may be well-written but are structurally irrelevant to what AI is trying to answer.
  • Missing coverage zones — prompts topics or angles that none of your chunks address at all.

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How to interpret the results

A low overall similarity score means your page is unlikely to be retrieved for that prompt, even if it ranks well on Google. A high overall score with several weak chunks suggests your content is partially aligned — AI may retrieve part of your page but miss the most authoritative sections. Look for patterns across multiple prompts. If your introductory chunks consistently score high but your supporting-evidence sections score low, you likely need to restructure how you present proof and specifics.
Chunk-level analysis surfaces gaps that keyword tools can’t detect. A section that uses the right words but structures them as a promotional narrative rather than a direct answer will score low — because it doesn’t match the embedding pattern of the content AI selects.

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Dominant claims

Dominant claims are the recurring assertions that AI makes when answering prompts in your market category — and the brands those assertions are attributed to. GenRank extracts these claims from response batches and maps them to specific brands. A claim like “the most accurate AI citation tracker” appearing repeatedly and attributed to a competitor tells you that competitor has effectively occupied that position in AI’s understanding of your category. Use dominant claims data to answer three questions:
  1. Which claims define your category? If a claim appears in the majority of AI responses for a prompt, it’s load-bearing for that topic. Not owning it means ceding positioning to whoever does.
  2. Who currently controls each claim? Attribution frequency shows where authority is concentrated. A claim attributed to one brand across most responses is entrenched.
  3. Are any valuable claims unattributed? Claims that appear frequently but aren’t consistently attributed represent positioning opportunities — no brand has yet established clear authority.

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Practical workflow

Use this workflow when you want to improve your retrieval rate for a specific prompt.
1

Select a prompt

Open Optimization → Content Retrieval and choose a prompt from your tracked list. Prioritize prompts where your mention rate is low or where a competitor appears significantly more often than you do.
2

Review the content requirements

Read through the requirements GenRank has extracted from real AI responses for that prompt. Note which answer formats, claims, and supporting elements appear most frequently — these are the highest-signal requirements.
3

Run vector similarity on your page

Enter the URL of the page you intend to rank for this prompt. Review the chunk-level similarity scores to see which sections align well and which don’t.
4

Identify structural gaps

Cross-reference low-scoring chunks with the content requirements list. If a high-frequency requirement (for example, a direct comparison table) has no corresponding chunk on your page, that’s a confirmed gap.
5

Update your content

Revise the page to address the gaps. Add the missing content elements, restructure weak sections so each chunk directly addresses a distinct aspect of the prompt, and ensure supporting evidence is concrete and specific rather than general.
6

Re-run similarity analysis

After publishing the updated page, re-run the vector similarity analysis to confirm your chunk scores have improved. Monitor your mention rate for that prompt over the following tracking cycles.

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Frequently asked questions

Traditional SEO focuses on keyword presence and technical ranking signals. Content Retrieval focuses on semantic alignment — whether your content structurally matches what AI selects when generating a response. A page can rank on page one of Google and still score poorly for AI retrieval if its structure doesn’t match the patterns AI favors for that prompt type.
GenRank re-analyzes response batches periodically and whenever your prompt’s tracking data shows a significant shift. You can also trigger a manual re-analysis from the Content Retrieval dashboard.
Yes. You can run vector similarity analysis on any publicly accessible URL, including competitor pages. This lets you understand why a competitor’s page is being retrieved over yours for a specific prompt.
GenRank splits pages by major heading sections and logical paragraph blocks. A typical blog post might produce 8–20 chunks. Very long pages may produce more. The chunking logic mirrors how LLMs break content during the retrieval phase of their inference pipeline.