Find the realistic AI prompts your page already answers well, the ones it's close to winning, and the gaps worth closing — each with a concrete recommended action. This is a content-fit and opportunity signal, not live AI search ranking. A page that already answers a prompt is a prerequisite for citation; it doesn't guarantee it.
You've fixed crawlability. Your content is visible. Performance is solid. Now the question shifts: if AI systems can access your page, what queries might they consider it relevant for? Prompt Discovery explores this question by analyzing your page content and structure to infer potential query matches.
Important caveat: this is exploratory, not predictive.
"What are people searching for?" — forward-looking, volume-driven, starting from the query. It tells you what exists in search demand.
"Given my existing content, what realistic prompts is the page already strong on, which is it close to winning, and which are the gaps worth closing?" Each prompt comes with a tier label and a concrete recommended action — so the list itself tells you where to start.
Understand what pages are actually good for, see the closest wins at a glance, and get a concrete content action for every gap. The tier label tells you what the page is doing today; the position in the list orders prompts by how realistic and valuable each one is.
Each content element you add unlocks new AI prompt matches. Watch how a bare page becomes a source AI systems trust.
| Model | Battery | ANC | Price |
|---|---|---|---|
| Acme Pro | 30hr | ★★★★★ | $299 |
| Sony XM5 | 30hr | ★★★★☆ | $349 |
| Bose QC45 | 24hr | ★★★★☆ | $329 |
Every prompt the page could realistically answer lands in one ranked list. Each card is tagged with what the page is doing on that prompt today — and every card has a concrete recommended action.
Page contains: product title "Premium Travel Headphones with Active Noise Cancellation," specs table (30hr battery, foldable), ANC technology explanation, comparison table (Sony/Bose/Apple), use cases, customer review about a 12-hour flight, and Product schema.
Product category states "travel," 30hr battery, foldable with case, customer review mentions long-haul flight, use case section highlights travel explicitly. Recommended action: protect and amplify what's working.
Dedicated ANC section with technical explanation, comparison of ANC effectiveness across models. Recommended action: keep the cited section concentrated and intact.
30hr battery is prominent, comparison table includes battery specs. The page doesn't yet lead with battery life — travel is the lead claim. Recommended action: add a short "battery life leaders" comparison row that explicitly ranks by hours.
Comparison table includes both brands but the head-to-head is limited to three models and lacks a verdict line. Recommended action: add a one-paragraph "which wins for what" call-out below the table.
Product is priced at $299 and marketed as premium. All comparison models are $200+. Recommended action: this prompt is out of scope for the current page — plan a separate budget-tier comparison page if you want to win it.
Page references the XM5, not the XM4. No repair content; the intent (repair) does not match the page intent (purchase). Recommended action: ignore — a repair guide belongs on a dedicated support page.
Already strong cards are validation — they confirm the page is doing its job. The real lift comes from the tiers below. Close to winning cards name the closest wins, with a recommended action that would push them to Already strong. Needs content work and Gap cards name realistic prompts the page doesn't yet answer well, with a concrete content action to close each one. The tier label tells you what state the page is in. The recommended action tells you what to do about it.
For each card, prompt discovery shows the specific content sections, data points, and structural elements that support the match. Here's the evidence breakdown for "best noise-canceling headphones for travel."
{
"@type": "Product",
"name": "Premium Travel Headphones
with Active Noise Cancellation",
"category": "Travel Audio"
}Schema explicitly identifies the product and machine-readably encodes the travel category—directly matching the query's primary intent.
Battery Life 30 hours Design Foldable + carry case Weight 250g Connectivity Bluetooth 5.2
Structured and easily extractable. The 30hr battery exceeds travel use case requirements; foldable design signals portability.
Heading: "Perfect for Long Flights." Body excerpt: 30hr battery, ANC blocks engine noise, folds flat for carry-on storage. Explicit use case that directly matches the query intent.
"Bought for a 12-hour flight to Tokyo. Battery lasted the entire trip with 20% remaining. Best travel headphones I've owned." Real-world validation of travel use case from a verified buyer.
All three competitors in the comparison table are labeled "Top Travel ANC," "Premium Travel Choice," and "Luxury Travel Option." This competitive framing reinforces the page's relevance to travel-focused queries.
Text extraction: headings, body, lists, tables, alt text, button and link text. Metadata extraction: title, description, OG tags, schema markup. Structural analysis: page type, section layout, spec tables, reviews, comparison tables.
Product pages: shopping, research, or troubleshooting. Articles: informational, how-to, current events. Service pages: hiring, comparison, local. Each pattern maps to a different set of likely user intents.
Question formats: What/Which/How, comparisons, best-of, problem-solution. Natural language patterns: conversational, specific detail-driven, intent modifiers like "for travel" or "under $50."
Direct quotes from body text, structured data points, schema properties, specification values, and contextual signals like competitor framing or use case headings.
Prompt Discovery shows content-based inferences only. It doesn't know whether AI systems will actually use your page in a response, how you rank against competing sources, or what personalization effects apply.
Keyword research is forward-looking and volume-driven—it starts from queries. Prompt Discovery is backward-looking and content-driven—it starts from what already exists on the page.
The tool shows current content-query alignment as it stands. It does not prescribe rewrites, suggest which keywords to add, or generate an optimization roadmap.
Many signals beyond content match determine actual AI retrieval: recency, authority, user context, competing sources, and query ambiguity all play a role.
Run on your top 20 blog posts. Identify which have clear prompt matches versus vague alignment. Flag strong-match posts for promotion. Identify gaps where key questions have no strong page match and prioritize content creation accordingly.
Compare prompt discovery results against your target keywords. Use the overlap—or absence of it—to validate whether your content actually supports the keywords you're targeting in traditional search.
List common questions customers ask about your product. Check which questions have a strong page match already. Identify the gaps. Commission new content specifically for unmatched high-value questions.
Run on your top 100 articles. Flag broad-potential articles with five or more Already strong prompts for promotion. Flag narrow-match articles and follow the recommended action on each Close to winning card to cover adjacent questions more completely.
Prompt Discovery is most useful when combined with the other diagnostics in BeSeenByAI. The checks work together.
Your page has strong content potential—five Already strong prompt cards—but robots.txt blocks GPTBot. None of that potential matters until the bot can access the page. Fix crawlability first.
Customer reviews are the strongest evidence for your Already strong prompt cards, but they only load via JavaScript. The raw HTML AI bots receive doesn't include them. The evidence exists—AI just can't see it.
Already strong prompt match, but structured data is missing and page structure is broken. The content alignment is real, but weak authority signals undercut credibility with AI systems.
Relevant content exists and the page is already strong on the prompt, but TTFB is 2,200ms. The page might time out before AI crawlers finish loading it. Performance problems erase content quality advantages.
Results should be treated as directional signals, not definitive assessments. The tier label reflects what the page is doing today, not predicted AI behavior — and the supporting section evidence is what makes findings actionable.
We analyze your page content to infer potential query matches. We do not query ChatGPT, Claude, Perplexity, or any AI system to validate results. These are content-based inferences only.
Current analysis focuses on English-language queries and content. Multi-language support is planned for a future release.
User context, personalization, recency, and query ambiguity all affect actual AI retrieval. Two users asking the same query may receive different results based on their history and context.
Good candidates: articles, guides, product pages, how-tos, FAQs.
Poor candidates: login pages, cart pages, thin pages, navigation-only pages with minimal body content.
Prompt Discovery is most useful after the basics are working. Use it to go deeper on pages that are already accessible and visible to AI systems.