Guides AI Prompts for SEO: 25 Evidence-Backed Prompts for Search & AI Visibility

AI SEARCH PROMPTS

AI Prompts for SEO: 25 Evidence-Backed Prompts for Search & AI Visibility

Use 25 practical AI prompts for keyword research, content, technical SEO and AI visibility. Each template shows what real data to provide and what not to invent.

By Rankvia ·

Illustration of SEO and AI-search evidence flowing through a central analysis system into prioritized actions.

AI prompts for SEO are most useful when they turn supplied evidence into a clearer decision. They are least useful when they invite a model to invent rankings, live SERPs, competitor data, traffic, or technical findings it cannot see.

This library contains 25 copyable templates for research and review. Replace the bracketed placeholders before using a prompt. Where a template needs live search results, analytics, Search Console, or AI-answer evidence, it says so explicitly.

How to use these AI search prompts for SEO

Before pasting a prompt, provide the minimum context it needs: the business, market, audience, offer, buyer question, relevant URL, and evidence. Ask for concise tables when you need comparison, and preserve the inputs alongside important decisions so the output remains explainable.

Prompts do not replace live research, first-party analytics, or recurring measurement. For the practical workflow around evidence-backed SEO and AI-search analysis, read AI Search Optimization.

1. Define the business context

Use this before research when the product, audience, or market is not yet clearly stated.

Use only the information below to create a concise SEO and AI-search context brief.

Business: [BUSINESS]
Offer: [PRODUCT OR SERVICE]
Primary audience: [AUDIENCE]
Market and language: [MARKET / LANGUAGE]
Primary buyer problem: [PROBLEM]
Known competitors: [COMPETITORS]
Relevant pages: [URLS OR PAGE SUMMARIES]

Return:
1. A one-sentence description of the offer.
2. The audience and buying context.
3. Five terms that must be used consistently.
4. Any ambiguity, contradiction, or missing information in the supplied inputs.

Do not invent product features, customers, competitors, search demand, or rankings.

2. Generate buyer questions, not keyword variations

This prompt creates a reviewable starting set. It does not claim those questions have search volume or are currently asked by users.

Create candidate buyer questions for this business context:

[PASTE THE APPROVED CONTEXT BRIEF]

Produce 20 questions across discovery, use case, audience-specific, alternatives, comparison, and purchase evaluation. For each question, include:
- buyer stage;
- underlying decision;
- why the question could matter commercially;
- whether it needs live search-demand validation before prioritization.

Avoid near-duplicates and branded questions unless they represent a real evaluation step. Do not claim these questions have live search volume or current AI visibility.

3. Review a buyer-question set for quality

Use this after drafting questions manually or with a model.

Review this buyer-question set against the business context below.

Business context:
[PASTE CONTEXT]

Questions:
[PASTE QUESTIONS]

Return a table with: question, buyer stage, commercial relevance, duplicate or overlap risk, poor-fit risk, missing context, and keep / revise / remove.

Do not add new questions unless a clear missing buyer decision is identified. Do not infer demand, rankings, or AI-answer behavior without supplied evidence.

APPLY THIS TO YOUR WEBSITE

See your AI visibility before repeating manual checks

Use a stable set of buyer questions to inspect where your brand, competitors, and sources appear across AI search.

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Rankvia checks ChatGPT, Gemini, and Google AI. No credit card required.

4. Classify a supplied AI-answer observation

Paste the answer text and any source links you observed. Do not ask a model to claim it saw an answer you have not supplied.

Analyze this observed AI answer for a buyer question.

Buyer question: [QUESTION]
Provider and date: [PROVIDER / DATE]
Market and language: [MARKET / LANGUAGE]
Observed answer text: [PASTE ANSWER]
Observed source links, if any: [PASTE LINKS]
Our brand and aliases: [BRAND / ALIASES]
Known competitors: [COMPETITORS]

Return a table that separates:
- brand mention;
- recommendation evidence;
- competitor mentions;
- competitor recommendation evidence;
- own-domain source evidence;
- competitor-owned source evidence;
- third-party sources;
- ambiguities that need human review.

Quote only the supplied answer text. Do not infer a hidden ranking mechanism or claim a citation caused a recommendation.

5. Diagnose a meaningful AI visibility gap

Use several supplied observations, not one volatile answer.

Diagnose this AI visibility pattern using only the evidence provided.

Business context: [CONTEXT]
Buyer question cluster: [QUESTIONS]
Observations by provider and date: [PASTE TABLE OR NOTES]
Existing relevant pages: [URLS / SUMMARIES]
Competitor and source evidence: [PASTE EVIDENCE]

Return:
1. What is observed versus interpreted.
2. Whether the pattern is recurring enough to investigate.
3. The likely action class: Fix access, Improve, Create, Third-party visibility, Monitor, or Skip.
4. The smallest next evidence check.

Do not assign a universal AI-readiness score or claim a specific change will cause an AI placement.

For the full evidence model, read AI Visibility Audit. For a small manual workflow, use the prompt library for an AI visibility audit.

6. Compare real AI-search competitors

Compare competitive evidence within this defined buyer-question universe.

Business: [BUSINESS]
Questions and providers: [PASTE OBSERVATIONS]
Known competitors: [COMPETITORS]
Observed brands and sources: [PASTE EVIDENCE]

Classify each entity as known direct competitor, observed AI competitor, substitute, incidental brand, competitor-owned source, or third-party source. Then identify recurring patterns by buyer question and provider.

Do not turn sources into competitors automatically. Do not treat Share of Voice as absolute visibility, and do not infer why another brand appeared more often.

For the full interpretation framework, see AI Search Competitor Analysis.

7. Map a gap to the right existing page

Map this buyer-question gap to the best page owner on our website.

Buyer question and evidence: [QUESTION / EVIDENCE]
Current site pages: [URL, TITLE, PURPOSE, SUMMARY FOR EACH PAGE]
Business context: [CONTEXT]

Return a table with the best existing owner, why it is or is not suitable, overlap risks, and one recommendation: Improve, Create, Monitor, Third-party visibility, or Skip.

Prefer improving the correct existing owner over creating a duplicate. Do not recommend a new page unless no existing page can reasonably serve the buyer need.

8. Analyze supplied Search Console or analytics data

This prompt requires an export or connected data. It cannot inspect your account itself.

Analyze the supplied first-party data. Do not claim access to Google Search Console, analytics, or live reporting outside this input.

Date range: [DATE RANGE]
Property or site: [SITE]
Data export: [PASTE CSV, TABLE, OR SUMMARY]
Relevant pages and buyer questions: [CONTEXT]

Return:
1. Important trends and anomalies.
2. Pages or queries needing investigation.
3. Data limitations, including missing dimensions or uncertain attribution.
4. A prioritized list of follow-up checks.

Do not attribute changes to an AI platform, a prompt, or a page edit unless the supplied evidence supports that conclusion.

9. Review current SERP evidence without fabricating it

Use this only after supplying live results from a search tool, browser, or export.

Review the live search evidence supplied below for this query.

Query, market, device, and date: [QUERY / MARKET / DEVICE / DATE]
Live SERP evidence: [PASTE RESULTS, URLS, TITLES, FEATURES, AND NOTES]
Our relevant pages: [URLS]
Business context: [CONTEXT]

Identify the apparent search intent, page types represented, gaps in our current coverage, and any page-ownership conflicts. Mark every conclusion as either supported by supplied evidence or needing verification.

Do not claim you searched the web, saw live rankings, or measured search volume unless those results are included above.

10. Turn evidence into a decision memo

Use this final prompt when the inputs have been reviewed, not as a substitute for research.

Write a decision memo from the supplied evidence.

Business objective: [OBJECTIVE]
Buyer-question evidence: [EVIDENCE]
AI visibility observations: [EVIDENCE]
Search and first-party data: [EVIDENCE]
Existing pages: [EVIDENCE]
Competitor and source patterns: [EVIDENCE]

Return: executive summary, verified findings, interpretations, unresolved questions, recommended action, expected effort, and measurement plan.

Use only Fix access, Improve, Create, Third-party visibility, Monitor, or Skip as action classes. Separate facts from interpretation. Do not fabricate metrics, customer outcomes, rankings, or causality.

11. Refresh an existing page with current evidence

Page: [PAGE]
Performance and current evidence: [DATA]
Classify each section as Keep, Update, Expand, Consolidate, Remove, or Verify. Prioritize outdated claims, unanswered questions, and material new evidence. Do not invent changed facts or rewrite content merely to make it look fresh.
Return the refresh plan in priority order.

For a deeper workflow, see Content Refresh.

12. Improve title and meta description from evidence

Page: [PAGE]
Current title and meta: [METADATA]
Relevant queries and live SERP evidence: [DATA]
Create three accurate title and meta alternatives. Do not invent dates, numbers, rankings, or benefits.
Return: Option | Title | Meta description | Why it fits the evidence.

Target page: [TARGET_PAGE]
Possible source pages: [SOURCE_PAGES]
Use only supplied source URLs. Suggest contextual placement, an anchor concept, and a reader benefit. Omit forced or redundant links. Never invent URLs or optimize around an arbitrary exact-match quota.
Return: Source URL | Destination | Suggested anchor | Placement | Reason.

14. Build a hub-and-spoke structure from real pages

Existing page inventory: [PAGES]
Group supplied pages by shared topic and reader intent. Identify existing hubs, supporting pages, ambiguous pages, isolated pages, and overlapping jobs. Label any missing hub as Proposed page, not an existing URL.
Return: Cluster | Existing hub | Supporting pages | Ambiguous pages | Possible missing owner.

15. Diagnose orphaned and underlinked pages

Business context: [CONTEXT]
Internal-link crawl: [CRAWL_DATA]
Identify true orphan candidates, weakly supported important pages, deep pages, and plausible source pages. Separate objective crawl observations from recommendations. Do not assume a low-link page is unimportant or invent links without a content relationship.

For broader methodology, see Internal Linking for SEO and AI Search.

16. Compare competing pages by buyer task

Buyer question: [QUESTION]
My page: [MY_PAGE]
Other pages: [COMPETITOR_PAGES]
Compare intent, audience, use-case coverage, evidence, clarity, and tradeoffs. Separate observable differences from strategic interpretation. Do not infer that word count, headings, schema, or backlinks caused visibility without causal evidence.

17. Find genuine content gaps

Business and audience: [CONTEXT]
Our pages: [MY_PAGES]
Other pages: [COMPETITOR_CONTENT]
Classify each candidate as Genuine missing buyer need, Already covered, Improve existing page, Competitor-only fit, or Insufficient evidence. Do not call a section on another page a gap automatically.

18. Separate competitors, substitutes, and sources

Business: [BUSINESS]
Known competitors: [KNOWN_COMPETITORS]
Observed brands and domains: [EVIDENCE]
Classify each as direct competitor, observed competitor, substitute, incidental brand, third-party source, or unclear. Recommend Track, Do not track, or Investigate. Do not turn publishers, directories, or communities into competitors automatically.

See AI Search Competitor Analysis for the complete framework.

19. Generate buyer questions for AI visibility

Business/product: [PRODUCT]
Customers: [AUDIENCE]
Use cases: [USE_CASES]
Market: [MARKET]
Competitors: [COMPETITORS]
Generate distinct buyer questions across discovery, use case, evaluation, alternatives, comparison, and selection. Do not mechanically turn keywords into questions or invent question popularity.
Return: Buyer question | Buyer stage | Decision | Why it matters.

For recurring methodology, see How to Track AI Visibility.

20. Classify actual AI visibility evidence

Tracked brand: [BRAND]
Buyer question: [QUESTION]
Observed answer: [RESPONSE]
Visible sources: [SOURCES]
Classify supplied evidence only: brand and competitor mentions, recommendation evidence, own-domain, competitor-owned, and third-party sources. Do not infer hidden training data, causal ranking factors, or treat every Sources-panel link as an equivalent citation.

21. Compare AI visibility across providers

Brand: [BRAND]
Question-level provider observations: [DATA]
Identify consistent appearance or absence, provider-specific gaps, recurring competitors, source patterns, and sparse evidence. Separate observed pattern from interpretation. Do not create a proprietary score or assume one provider explains another.

For relative measurement context, see AI Share of Voice.

22. Analyze recurring AI source patterns

Tracked brand: [BRAND]
Source observations: [SOURCE_DATA]
Classify sources as own domain, competitor-owned, publisher, directory, marketplace, community, institution, or unclear. Identify recurring and actionable patterns. A source is not automatically a competitor, and appearing on one does not guarantee a recommendation.

23. Diagnose an AI visibility gap

Business: [BUSINESS]
Buyer question: [QUESTION]
Observed AI responses: [AI_EVIDENCE]
Competitor, source, and existing-page evidence: [EVIDENCE]
Separate observations, missing evidence, interpretation, and one primary action: Improve, Create, Third-party visibility, Monitor, or Skip. Do not infer hidden AI ranking factors or claim a page caused a recommendation.

Read AI Visibility Audit and How to Improve AI Visibility for the full diagnostic and action frameworks.

24. Triage a real crawl export

Site context: [CONTEXT]
Crawl export: [CRAWL_DATA]
Group supplied rows into issue classes. State objective evidence, quantify scope only from supplied rows, distinguish likely issues from intentional behavior, set High, Medium, Low, or Investigate, and list verification before remediation. Do not invent traffic impact, ranking loss, or unobserved crawl behavior.

25. Review directives, redirects, and structured markup

Page objective: [CONTEXT]
Technical evidence: [TECHNICAL_DATA]
Review supplied robots rules, canonicals, redirects, status, markup, and known index state. Separate observations, potential problems, missing information, verification, and justified fixes. Do not infer ranking impact or actual crawl/index behavior from directives alone.

What not to ask AI to invent

If a decision depends on a number from a real system, supply the real number. Use a keyword dataset for search volume, a Search Console export for clicks and impressions, live SERP evidence for current rankings, analytics for conversions, and tracked observations for AI visibility.

When prompts are not enough

Prompts can help define questions, organize evidence, and review a small set of observations. They become fragile when you need recurring scans, multiple providers, a larger question set, competitor history, consistent source extraction, and a reproducible action workflow.

See practical AI prompts for generating and reviewing buyer questions. Then use recurring measurement when the evidence must stay comparable over time.

APPLY THIS TO YOUR WEBSITE

Move from prompt output to recurring evidence

Rankvia turns buyer-question visibility, competitors, and sources into a repeatable workflow for deciding what deserves action.

Explore Rankvia AI VisibilityRead the measurement framework

Frequently asked questions

Can I use these prompts with any AI tool?

Yes, provided you supply the required context and evidence. The prompts do not assume a specific model can access your private data, live SERPs, analytics, or accounts.

Should I ask an AI tool to find live SEO data?

Only if the tool has genuine web or data access and you verify the cited evidence. Otherwise, paste the current results or export you want reviewed and ask the model to distinguish evidence from interpretation.

Do prompts replace AI visibility tracking?

No. They are useful for a focused manual review. Recurring tracking is more reliable when you need comparable observations across questions, providers, dates, competitors, and sources.

Why do the prompts separate mentions, recommendations, and sources?

They answer different questions. A brand can be mentioned without being recommended, and a page can be shown as a source without its brand being recommended.

APPLY THIS TO YOUR WEBSITE

Check your AI visibility with real buyer questions

Start with evidence about where your brand appears, then decide whether a manual prompt workflow or recurring measurement is the better fit.

Check my AI visibilityExplore Rankvia AI Visibility

Free AI Visibility check. No credit card required.

Sources

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