Find the AI-Search Queries You Should Own But Don't
Build a content roadmap from a gap analysis: which AI-search queries match your expertise but currently cite weak sources, no source, or competitors you can credibly displace. Outputs a prioritized 90-day publishing plan with each query mapped to a content type, target schema, and the existing AlignAI prompt to execute it.
Pro tip
Most small businesses optimize for queries they already rank for, missing the much bigger opportunity: queries where AI engines are citing thin or generic sources because nobody specific has owned the topic yet. These "vacant authority" queries are won in 30–60 days with one well-structured page each. The hard part isn't writing — it's identifying which 5–10 queries to bet on.
How to use this prompt
- Pick your AI model. Choose the tab for Claude, ChatGPT, Gemini or Copilot — each variant is tuned for that model.
- Copy the full prompt. Click Copy Full Prompt to copy the text to your clipboard.
- Paste into your AI tool. Open your chosen model and paste the prompt into a new chat.
- Replace the
[placeholders]. Swap any bracketed fields for your company name, audience, product or tone. - Run and refine. Review the output. If anything is off, ask the AI to tighten tone, length or format.
Prompt Variants by Model
Claude
Claude 4.x
FRESH APR 2026
You are a content strategist who specializes in AI-search query gap analysis. Help me build a 90-day publishing roadmap targeting AI-search queries that match my expertise but currently cite weak or...You are a content strategist who specializes in AI-search query gap analysis. Help me build a 90-day publishing roadmap targeting AI-search queries that match my expertise but currently cite weak or competitor-owned sources I can credibly displace. Agentic — work the full workflow; don''t shortcut to a generic content list.
<my_context>
Business: [NAME] — [WHAT YOU DO IN ONE SENTENCE]
Specific expertise areas (3–5 narrow topics, not "marketing"): [e.g. "13-week cash flow forecasting for ecommerce", "Texas dental compliance audits", "abandoned cart recovery for Shopify Plus"]
Geographic / market focus: [LOCAL CITY / REGION / COUNTRY / GLOBAL / INDUSTRY]
Customer who would convert if they found us: [SPECIFIC TYPE]
Primary conversion goal of new content: [book consultation / email signup / purchase / trial / SQL]
Existing content I''m proud of (URLs of my best 3–5 pages): [URLs]
Existing content I''m embarrassed by (URLs of weak pages I should rewrite vs cut): [URLs or NONE]
My main competitors who get cited often: [LIST 3–5 COMPETITORS — names + domains]
Months I''m willing to commit to this roadmap: [3 / 6 / 12]
Realistic publishing capacity per month: [N pages of long-form content]
</my_context>
<query_seed_input>
For each of my expertise areas, paste the top ChatGPT, Perplexity, or Google AI Overview response to a query you''d expect customers to type. Aim for 3–5 candidate queries per expertise area (so 15–25 candidates total).
Format for each:
EXPERTISE AREA 1: [TOPIC]
- Candidate query: [VERBATIM]
- ChatGPT response + cited sources: [PASTE]
- Quality score (your gut, 1–5): [SCORE — does the AI response look weak/thin?]
- Candidate query: [VERBATIM]
- ChatGPT response + cited sources: [PASTE]
- Quality score: [1–5]
[Repeat for each candidate query and each expertise area]
</query_seed_input>
<scoring_rubric>
For every candidate query, score on three axes:
1. **Vacancy** (0–4): How weak / thin / generic are the currently-cited sources?
- 4 = no specific source cited, generic listicle, or AI hallucinated
- 3 = one weak source (Wikipedia stub, one thin blog post)
- 2 = competent but generic mid-tier sources (HubSpot, Investopedia, etc.)
- 1 = strong category-specific sources (industry-leading blogs, .edu/.gov)
- 0 = an entrenched citation incumbent owns this query
2. **Expertise match** (0–4): How directly does my listed expertise let me write the better answer?
- 4 = direct, narrow expertise area; I have proprietary data or methodology
- 3 = adjacent expertise; I''d need to do a small amount of research
- 2 = general industry knowledge; would feel generic
- 1 = stretch
- 0 = outside my lane
3. **Conversion proximity** (0–2): How close is the query to my primary conversion goal?
- 2 = bottom-of-funnel buying-stage query
- 1 = mid-funnel comparison/evaluation query
- 0 = top-of-funnel awareness-only query
Total = Vacancy + Expertise + Conversion (max 10). Queries scoring 7+ are roadmap candidates.
</scoring_rubric>
Output exactly five parts:
**PART 1 — Scored Candidate Pool**
Markdown table: # · Query · Vacancy · Expertise · Conversion · Total · Verdict (Roadmap / Maybe / Cut). Sort descending by Total.
**PART 2 — The Roadmap (top queries scoring 7+)**
For each roadmap query, one structured row:
- Query
- Recommended content type (FAQ page / how-to / comparison / data report / definitive guide / glossary entry / case study)
- Target schema (FAQPage / HowTo / Article / TechArticle / Product / etc.)
- Required proof point or unique data I''ll need to bring
- Estimated effort (Hours / Days)
- Which AlignAI prompt to use to execute it (name the specific prompt from the AI Search & SEO category — e.g. "Write a How-To Guide AI Engines Will Cite", "Generate FAQs Your Customers Actually Search For")
- Expected time-to-first-citation after publish (Days / Weeks / Months)
**PART 3 — 90-Day Publishing Schedule**
Monthly breakdown (Month 1 / Month 2 / Month 3) sized to my realistic capacity. Sequence with the highest-leverage / fastest-to-cite queries first.
**PART 4 — Cut, Rewrite, or Keep**
For each URL in my "embarrassed by" list, recommend Cut / Rewrite (which roadmap query it could be repurposed to serve) / Keep with light update.
**PART 5 — What I''ll Need From You (the user) Before Starting**
Bulleted list of inputs to gather before kicking off Month 1 — e.g. proprietary data, customer interviews, screenshots, methodology documentation. Be specific so the user can pre-collect.
End with the cadence to re-run this analysis (suggest based on category dynamics — quarterly is typical).
Notes: Claude is the strongest variant for the multi-axis scoring. The Vacancy axis is where it most outperforms other models — it's rigorous about distinguishing "weak" from "entrenched" cited sources.
ChatGPT
GPT-5
FRESH APR 2026
Act as a content strategist for AI-search query gap analysis. Build me a 90-day publishing roadmap targeting AI-search queries that match my expertise but currently cite weak or competitor-owned...Act as a content strategist for AI-search query gap analysis. Build me a 90-day publishing roadmap targeting AI-search queries that match my expertise but currently cite weak or competitor-owned sources I can credibly displace. Agentic — work the full workflow.
My context:
- Business + what we do: [DESCRIPTION]
- Specific expertise areas (3–5 narrow topics, not "marketing"): [LIST]
- Geographic/market focus: [SCOPE]
- Customer who''d convert: [SPECIFIC TYPE]
- Primary conversion goal: [book / email / purchase / trial / SQL]
- 3–5 best existing pages (URLs): [URLs]
- Weak pages to rewrite vs cut (URLs): [URLs or NONE]
- 3–5 competitors who get cited often: [NAMES + DOMAINS]
- Months committed: [3 / 6 / 12]
- Publishing capacity per month: [N pages of long-form]
Query seed input — for each expertise area, paste 3–5 candidate queries (15–25 total) with each engine''s response:
EXPERTISE AREA 1: [TOPIC]
- Candidate query: [VERBATIM]
- ChatGPT response + cited sources: [PASTE]
- Quality score (your gut, 1–5): [SCORE]
- Candidate query: [VERBATIM]
- ChatGPT response + cited sources: [PASTE]
- Quality score: [1–5]
[Repeat for each candidate + each area]
Score every candidate on three axes:
1. Vacancy (0–4): how weak/thin/generic the currently-cited sources are.
2. Expertise match (0–4): how directly my expertise lets me write the better answer.
3. Conversion proximity (0–2): how close to my primary conversion goal.
Total max 10. Queries scoring 7+ are roadmap candidates.
Output:
1. Scored Candidate Pool table: # · Query · Vacancy · Expertise · Conversion · Total · Verdict (Roadmap / Maybe / Cut). Sort descending by Total.
2. The Roadmap (queries scoring 7+) — per query: content type (FAQ / how-to / comparison / data report / definitive guide / glossary / case study), target schema, required proof point or unique data, effort (Hours/Days), which AlignAI prompt to execute it, expected time-to-first-citation.
3. 90-Day Publishing Schedule — Month 1 / 2 / 3, sized to my capacity, highest-leverage first.
4. Cut, Rewrite, or Keep — for each "embarrassed by" URL: Cut / Rewrite (mapped to which roadmap query) / Keep with light update.
5. What I''ll Need Before Starting — bulleted inputs to gather (proprietary data, customer interviews, screenshots, methodology). Specific.
End with cadence to re-run.
Notes: GPT-5 with browsing can verify the cited-sources quality assessments directly. Best with browsing enabled.
Gemini
Gemini 2.5 Pro
FRESH APR 2026
You are a content strategist for AI-search query gap analysis. Build me a 90-day publishing roadmap targeting AI-search queries that match my expertise but currently cite weak or competitor-owned...You are a content strategist for AI-search query gap analysis. Build me a 90-day publishing roadmap targeting AI-search queries that match my expertise but currently cite weak or competitor-owned sources I can credibly displace.
MY CONTEXT:
• Business + what we do: [DESCRIPTION]
• Specific expertise areas (3–5 narrow topics): [LIST]
• Geographic/market focus: [SCOPE]
• Customer who''d convert: [TYPE]
• Primary conversion goal: [book / email / purchase / trial / SQL]
• 3–5 best existing pages: [URLs]
• Weak pages to rewrite vs cut: [URLs or NONE]
• 3–5 competitors getting cited often: [NAMES + DOMAINS]
• Months committed: [3 / 6 / 12]
• Publishing capacity per month: [N pages]
QUERY SEED INPUT — for each expertise area, paste 3–5 candidate queries (15–25 total) with engine responses:
EXPERTISE AREA 1: [TOPIC]
- Candidate query: [VERBATIM]
- ChatGPT response + cited sources: [PASTE]
- Quality score (gut, 1–5): [SCORE]
- Candidate query: [VERBATIM]
- ChatGPT response + cited sources: [PASTE]
- Quality score: [1–5]
[Repeat]
Score every candidate on three axes:
1. Vacancy (0–4): how weak/thin/generic the currently-cited sources are.
2. Expertise match (0–4): how directly my expertise wins this query.
3. Conversion proximity (0–2): how close to my conversion goal.
Total max 10. Roadmap candidates score 7+.
OUTPUT:
1. Scored Candidate Pool table: # | Query | Vacancy | Expertise | Conversion | Total | Verdict (Roadmap / Maybe / Cut). Sort descending.
2. The Roadmap (7+ queries) — per query: content type, target schema, required proof point, effort (Hours/Days), which AlignAI prompt, expected time-to-first-citation.
3. 90-Day Schedule — Month 1 / 2 / 3 sized to capacity, highest-leverage first.
4. Cut / Rewrite / Keep — for each "embarrassed by" URL: action + which roadmap query it serves if rewritten.
5. What I''ll Need Before Starting — bulleted, specific inputs to pre-collect.
End with cadence to re-run.
Notes: Gemini 2.5 Pro with Deep Research is excellent for verifying the cited-sources quality across all 5 AI engines. Use Deep Research mode if available.
Copilot
Copilot (GPT-5)
FRESH APR 2026
Build a 90-day AI-search content roadmap targeting queries that match my expertise but currently cite weak or competitor-owned sources I can displace.
My context:
• Business + what we do:...Build a 90-day AI-search content roadmap targeting queries that match my expertise but currently cite weak or competitor-owned sources I can displace.
My context:
• Business + what we do: [DESCRIPTION]
• Specific expertise areas (3–5 narrow topics): [LIST]
• Geographic/market focus: [SCOPE]
• Customer who''d convert: [TYPE]
• Primary conversion goal: [book / email / purchase / trial / SQL]
• 3–5 best existing pages: [URLs]
• Weak pages to rewrite vs cut: [URLs or NONE]
• 3–5 competitors getting cited often: [NAMES + DOMAINS]
• Months committed: [3 / 6 / 12]
• Publishing capacity per month: [N pages]
Query seed input — for each expertise area, paste 3–5 candidate queries (15–25 total) with engine responses:
EXPERTISE AREA 1: [TOPIC]
- Candidate query: [VERBATIM]
- ChatGPT response + cited sources: [PASTE]
- Quality score (1–5): [SCORE]
- Candidate query: [VERBATIM]
- ChatGPT response + cited sources: [PASTE]
- Quality score: [1–5]
[Repeat]
Score on three axes:
1. Vacancy (0–4): how weak/thin/generic the cited sources are.
2. Expertise match (0–4): how directly my expertise wins.
3. Conversion proximity (0–2): closeness to conversion goal.
Total max 10. Roadmap candidates score 7+.
Output:
1. Scored Candidate Pool table: # · Query · Vacancy · Expertise · Conversion · Total · Verdict. Sort descending.
2. The Roadmap (7+) per query: content type, target schema, required proof point, effort (Hours/Days), which AlignAI prompt, expected time-to-first-citation.
3. 90-Day Schedule: Month 1 / 2 / 3 sized to capacity, highest-leverage first.
4. Cut / Rewrite / Keep for each "embarrassed by" URL.
5. What I''ll Need Before Starting — bulleted, specific.
End with cadence to re-run.
Notes: Copilot is reliable for the 90-day scheduling step. Particularly good when planning for Microsoft-stack content workflows (Word + SharePoint).
Frequently Asked Questions
What does the Find the AI-Search Queries You Should Own But Don't prompt do?
Build a content roadmap from a gap analysis: which AI-search queries match your expertise but currently cite weak sources, no source, or competitors you can credibly displace. Outputs a prioritized 90-day publishing plan with each query mapped to a content type, target schema, and the existing AlignAI prompt to execute it.
Which AI models is this prompt tested on?
This prompt is field-tested on Claude, ChatGPT, Gemini and Copilot. Each model has its own optimized variant above.
Do I need a paid AI account to use this prompt?
No. This prompt is written to run on the free tier of Claude, ChatGPT, Gemini and Copilot. Paid tiers simply give you longer context windows and faster responses.
Can I customize this prompt for my business?
Yes. Any text inside square brackets is a placeholder you replace with your own business details, such as company name, audience, product or tone. You can also ask the AI to adjust format, length or style after the first output.
When was this prompt last verified?
Each model variant above shows its own freshness stamp. AlignAI re-verifies every prompt at least monthly and rebuilds when a major model changes.
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