How do I choose the buyer questions to test first?
Start with the questions buyers ask when deciding whether a feature solves a specific problem, not with the feature name itself. A feature page should be tested against questions about use cases, constraints, alternatives, integrations, and outcomes.
Create a small prompt set from sales calls, site search, support tickets, comparison pages, and Search Console queries. Separate prompts that ask what a feature does from prompts that ask which product suits a situation. The second group usually reveals whether an assistant can connect your feature to a buyer's need.
Check whether every prompt has a clear audience, job, and decision. Remove vague prompts such as “tell me about automation” and replace them with questions that contain the category and use case. Also record important qualifiers, such as company size, technical environment, budget sensitivity, or required integration. Those qualifiers can change which pages an engine cites.
Cituna tracks prompts across ChatGPT, Perplexity, Gemini, Claude, Grok, Google AI Overviews, and Google AI Mode every day. Whether you use Cituna or manual testing, begin with a stable prompt set so later changes can be compared against the same buyer questions.
For more context, read Ai Search Technical Seo Checklist What To Fix First.
Which feature page should answer each buyer question?
Assign one primary feature page to each buyer question before changing copy. The right page is the one that can prove the answer with specific product facts, not simply the page with the closest keyword.
Check whether the page explains the feature's job, who needs it, what it works with, and where its limits apply. A page titled after an internal feature label may be a poor answer for a question framed around a business problem. In that case, the feature page may need a clearer opening explanation, while a broader use-case page may deserve the citation.
Create a simple mapping with four fields: buyer question, intended page, supporting evidence, and competing page currently cited. Look for collisions where several pages make similar claims. Conflicting descriptions weaken the signal that one page is the authoritative answer.
Do not force every question onto a feature page. If the question requires pricing, implementation guidance, security details, or a comparison, another page may be more appropriate. The decision is successful when the intended page can answer the question directly without requiring an assistant to combine scattered claims.
For more context, read How Often Should I Check Ai Visibility.
What evidence is missing from the feature page?
Add evidence that lets an assistant verify the feature's usefulness without inferring it from marketing language. Strong evidence usually includes a precise capability, the condition under which it works, the user problem it addresses, and a concrete boundary.
Check the opening section first. It should state what the feature does and when a buyer would choose it. Then check the body for supported integrations, inputs, outputs, workflows, limitations, and links to relevant documentation. Replace broad claims such as “powerful” or “seamless” with statements that describe an observable action.
The most commonly skipped check is the relationship between a feature and its outcome. A page may explain that a tool exports data but never say which decision the export supports. Add that connection only when the product can substantiate it. Unsupported outcome claims create ambiguity rather than visibility.
Compare the page with the passages cited for the same question. If competitors provide a short definition, a use case, and a limitation in one passage, make your equivalent evidence equally easy to locate. Do not copy their wording. The goal is to remove the evidence gap that causes an engine to prefer another source.
Should I change the feature page or create a new page?
Change the existing feature page when the page already owns the feature and only lacks a clear buyer-facing explanation. Create a new page when the question represents a distinct job, audience, or comparison that the feature page cannot answer without becoming confusing.
Check three signals before deciding. First, can the existing page answer the question in its first section? Second, would adding the missing context make the page useful to the people who arrive through its current intent? Third, does another page already cover the broader use case? If the answers are yes, yes, and no, improve the existing page.
A new page is more suitable when one feature serves materially different audiences or when a use-case question requires a workflow, implementation detail, or evaluation criteria. Link the new page to the feature page and use consistent terminology so engines can connect the relationship.
Avoid creating near-duplicate pages for every prompt variation. Multiple pages with overlapping claims can split authority and make internal ownership unclear. The practical decision rule is simple: consolidate when the evidence and audience are the same; separate when the decision, proof, or audience is meaningfully different.
How do I identify why another page gets cited?
Compare the cited competitor passage with your intended feature-page passage, rather than comparing domains in general. The useful question is not which brand is more visible. It is what information the cited page makes easier to retrieve and trust.
Check four differences in order: wording, placement, specificity, and context. Wording shows whether the competitor describes the buyer's problem while your page describes an internal feature. Placement shows whether the answer appears near the top or is buried below promotional copy. Specificity shows whether the cited page names conditions, integrations, or limits. Context shows whether the passage is supported by related pages.
Also check whether the competitor page is actually answering a different question. An engine may cite a comparison, help article, or use-case page because the prompt calls for evaluation, even when your feature page contains the product fact. That is a page-selection problem, not necessarily a missing-keyword problem.
Cituna shows which competitors and pages the seven tracked engines cite instead of your brand. Use that evidence to choose one change at a time. If the rival wins on a missing limitation, add a limitation. If it wins because the prompt needs a comparison, improve the appropriate comparison or use-case page instead of overloading the feature page.
What technical checks should come before rewriting?
Confirm that the intended feature page can be crawled, rendered, indexed, and reached through ordinary links before treating a visibility gap as a content problem. A well-written page cannot be cited reliably if the relevant text is unavailable to retrieval systems.
Check the page's indexation status, canonical target, robots directives, response behavior, and rendered text. Make sure the primary explanation is present in the page content rather than appearing only after an interaction, inside an image, or behind a restricted interface. Check that internal links use descriptive anchor text and that the page is not isolated from the rest of the site.
Review changes after publishing. A canonical change, template deployment, JavaScript error, or access rule can remove visibility without any editorial change. Search Console can help identify search-side issues, but AI answer behavior still needs direct prompt testing across the relevant engines.
Do not assume that structured data alone will solve a feature-page problem. Markup can clarify entities and page types, but it cannot supply missing product evidence or make an unsuitable page answer a buyer's question. Fix access and rendering issues first, then evaluate whether the page's explanation matches the intended prompt.
How should I compare fixes before making several changes?
Compare one meaningful change against a fixed prompt set before combining multiple rewrites, technical changes, and new pages. Isolating the change gives you a better explanation for why an engine's answer changed.
Record the original answer, cited URLs, cited passages, brand presence, and the page intended to answer each prompt. After publishing, repeat the same prompts across ChatGPT, Perplexity, Gemini, Claude, Grok, Google AI Overviews, and Google AI Mode. Record whether the answer now describes the feature accurately, cites the intended page, or merely mentions the brand without useful evidence.
Check quality, not just inclusion. A citation to a feature page is not a success if the answer assigns the wrong capability, omits a limitation, or recommends the feature for an unsuitable use case. A competitor citation may remain correct for some prompts, and forcing your page into those answers can reduce trust.
Use Search Console alongside answer results to see whether a page change affects relevant search queries and clicks. Cituna joins answer observations with Google Search Console data, which can help connect visibility changes with search behavior. Treat that connection as diagnostic evidence, not proof that one channel caused the other.
When should I keep, revise, or retire the feature page?
Keep a feature page when it consistently explains a real capability and attracts the right buyer questions, revise it when answers are inaccurate or competitors supply clearer evidence, and retire or merge it when the feature no longer has distinct intent.
Check the page against the original prompt map after each review cycle. Keep it when the intended engines associate the page with the right questions and the cited passage is accurate. Revise it when the page is relevant but loses on clarity, proof, terminology, or limitations. Merge it when another page serves the same audience and decision with stronger evidence.
Retirement requires more than removing a weak URL. Check backlinks, internal links, search demand, existing citations, and references from documentation or support content. Redirect or replace the page only when the destination answers the same need. Otherwise, an apparent cleanup can turn a known source into a broken path.
A feature page should not be judged by brand mentions alone. The final check is whether an assistant gives a buyer a correct reason to consider the feature and points to a page that supports that reason. If the answer remains wrong after repeated content and access checks, reconsider whether the feature page is the right destination for that question.
Related reading
Sources consulted
- Google Search Central (developers.google.com)
- OpenAI (platform.openai.com)
- Perplexity (docs.perplexity.ai)
- Anthropic (anthropic.com)
Drafted with AI assistance from our own research and Search Console data, and reviewed by Rahul A before publishing. Rules and prices change; check the linked official source before you act.