How to Prioritize Content When Prompt Search Volume Is Unknown

When you plan content for AI-assisted search, you may not have a reliable count of how often people ask a particular question. A prompt is the question someone gives an AI search product. Its volume is difficult to measure across products, and a list of prompts you test does not tell you how many people ask them.

For GEO, short for generative engine optimization, choose topics based on evidence that readers need them, their relevance to your business, gaps in your existing pages, and your ability to give a useful answer. Use those signals to guide editorial choices. They do not replace prompt-volume data or predict which pages AI products will cite.

Why prompt counts are hard to obtain

An AI search product may use a question to retrieve information in ways that do not match the words the user entered. Google describes its AI Search features as using “query fan-out,” which breaks a question into related searches to gather information. A user’s prompt, the searches used to find sources, and underlying audience demand describe three different things: what the user asked, how the product retrieves sources, and how much people want the topic. So do not use a few test prompts to estimate market size. This example also does not show how every AI product works. (Google’s AI search guidance)

Conventional search data also leaves gaps. Google Search Console reports impressions and clicks, along with page and query data. Google says some anonymized queries do not appear in query tables. A short query list therefore does not prove that few people searched. Google Trends can help compare interest, but it uses sampled, normalized data; a low-volume term can show as zero. Trends also excludes searches made inside Google’s AI Mode and AI Overviews. (Search Console data limitations and Google Trends data details)

Use each source only for what it measures. Search Console describes recorded Google search activity, Trends compares relative interest, and prompt tests show what an AI product returned during those tests. None provides a complete count of all questions across AI search.

Compare topics using four kinds of evidence

To prioritize GEO content, start with topics you might cover, not individual prompt variations. Group questions that serve the same reader need, then assess each topic using four considerations:

Use the table to compare topics, not to calculate a supposedly objective “GEO score.” A topic with strong business relevance and a clear gap can deserve attention even when direct search evidence is sparse. Record the uncertainty so later decisions do not mistake a reasonable editorial choice for proven demand.

For example, imagine a software company receives repeated support questions about whether its product works with a particular platform. The company’s site has no clear answer, but Search Console shows little query data. The support questions establish a real customer need; they do not establish broad search demand. A focused compatibility page may still be a sound choice if the company can document the answer and keep it current.

Check the evidence you already have

Start with Google Search Console. Review the queries and pages related to each topic, and compare query-table rows with the report’s totals. Look at trends over time rather than treating a single period as definitive. Search Console can show Google search activity, but its query data does not reveal every search or every prompt that led to an AI answer.

Then inspect what visitors and customers ask after they reach you. Site-search terms, support requests, sales or intake questions, and behavior on relevant pages can show where people need clarification. These sources represent different groups and contexts, so keep them separate in your notes. Google explains that Search Console and Google Analytics measure different parts of the visitor journey, from search activity to on-site behavior and conversions. (Google’s comparison of Search Console and Analytics)

Where available, Bing Webmaster Tools’ AI Performance report adds another kind of evidence. As of November 11, 2026, Microsoft says the report shows cited pages and sampled grounding queries, the queries used to find supporting information, across supported AI experiences. These observations can help you see which topics appear in reported citations and where your pages are absent. Microsoft also cautions that citation counts do not show a page’s rank, authority, or role in a particular answer. Use the report to diagnose visibility, not to estimate total demand or business impact. (Bing AI Performance report)

You can also test a small set of realistic questions in the AI products relevant to your audience. Record the product, date, wording, answer, and cited sources. Use the results to find content gaps and see which sources appear. Each test shows what happened in that observation. It does not measure how often people ask the question.

Improve a page before creating another one

When a topic matters but your evidence is thin, inspect the page that already serves the closest reader need. If the page contains the answer but hides it, clarify the relevant section. If the information is stale, update it. If the existing page serves a different task and a new page would give readers a distinct answer, create a focused page.

Do not publish one article for every wording variation in a prompt list. Several phrasings can point to the same decision, and separate pages may repeat the same material without helping readers. Google’s guidance for its AI Search features emphasizes existing Search fundamentals and useful content; it does not require special AI-specific technical optimizations. (Google’s guidance for AI features)

Choose the topic where you can make the clearest improvement now. That might mean strengthening an existing product page, adding a well-supported comparison, or answering a recurring customer question. If you cannot provide accurate detail or evidence, defer publication rather than filling the gap with generic material.

Where prioritization goes wrong

Treating a zero in Google Trends as proof of no demand is a common mistake. Low-volume terms can register as zero, and Trends is sampled and normalized. Likewise, a small Search Console query table is incomplete because anonymized queries can be omitted.

Another mistake is to turn prompt tests or AI citations into demand estimates. A prompt test gives a qualitative check, while a citation report shows where pages appeared. Neither establishes how many people ask a question, how many visits a citation produces, or whether it generates leads or sales. Track search performance, AI visibility, visits, and conversions as separate outcomes.

Finally, do not treat technical access as proof that a page is visible in an answer. A page’s ability to load or meet Search eligibility conditions does not guarantee that a search product will index, retrieve, cite, or recommend it. Google says its AI features use existing Search eligibility and systems, but eligibility alone does not promise inclusion. (Google’s AI feature requirements)

Key Takeaways

• Prioritize topics, not prompt wording: Group questions around the reader’s underlying need so you do not create repetitive pages for phrasing variations.
• Use evidence for its actual purpose: Search Console, Trends, customer questions, prompt tests, and AI citation reports each measure different things.
• Choose work with a clear case: Compare relevance, evidence of need, coverage gaps, and your ability to answer accurately. Record uncertainty instead of inventing a score.
• Improve the closest useful page first: Create a new page only when it serves a distinct reader task and adds specific, supportable information.
• Measure outcomes separately: A citation, a visit, and a conversion are different results, and none alone proves the others.

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