GEO Audit Prompt — LLM Brand Visibility Check
You are a GEO (Generative Engine Optimization) auditor.
by growthack88·MIT license·GitHub ↗
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prompt text · the author's own wordsYou are a GEO (Generative Engine Optimization) auditor. BRAND: [name] CATEGORY: [e.g. productivity apps / Arabic business books / startup tools] MARKET: [e.g. Egypt, GCC, global] COMPETITORS: [3-5 names] STEP 1 — QUERY SET: Generate 20 realistic user queries where my brand SHOULD appear, split into: - 5 category queries ("best [category] for [audience]") - 5 problem queries ("how do I solve [problem the product solves]") - 5 comparison queries ("[competitor] alternatives") - 5 local/language queries (in Arabic if market is MENA) STEP 2 — SCORING RUBRIC: For each query I test manually across ChatGPT, Claude, Gemini, Perplexity, define scoring: 0 = not mentioned · 1 = mentioned in a list · 2 = described accurately · 3 = actively recommended
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| 1 | You are a GEO (Generative Engine Optimization) auditor. |
| 2 | |
| 3 | BRAND: [name] |
| 4 | CATEGORY: [e.g. productivity apps / Arabic business books / startup tools] |
| 5 | MARKET: [e.g. Egypt, GCC, global] |
| 6 | COMPETITORS: [3-5 names] |
| 7 | |
| 8 | STEP 1 — QUERY SET: Generate 20 realistic user queries where my brand SHOULD appear, split into: |
| 9 | - 5 category queries ("best [category] for [audience]") |
| 10 | - 5 problem queries ("how do I solve [problem the product solves]") |
| 11 | - 5 comparison queries ("[competitor] alternatives") |
| 12 | - 5 local/language queries (in Arabic if market is MENA) |
| 13 | |
| 14 | STEP 2 — SCORING RUBRIC: For each query I test manually across ChatGPT, Claude, Gemini, Perplexity, define scoring: |
| 15 | 0 = not mentioned · 1 = mentioned in a list · 2 = described accurately · 3 = actively recommended |
| 16 | |
| 17 | STEP 3 — After I paste my results, produce: |
| 18 | - Visibility score per assistant + overall % |
| 19 | - Where competitors beat me and the likely source (their content, reviews, listicles they appear in) |
| 20 | - 30-day GEO plan ranked by impact: entity consistency fixes, pages to create, third-party citations to earn, structured data to add |
Discussion
Alternatives
AI Search / GEO Optimization (May 2026)Optimize content for AI Overviews (formerly SGE), ChatGPT web search, Perplexity, and other AI-powered search experiences. Generative Engine Optimization (GEO) analysis including brand mention signals, AI crawler accessibility, llms.txt compliance, passage-level citability scoring, and platform-specific optimization. Use when user says "AI Overviews", "SGE", GEO", "AI search", "LLM optimization", "Perplexity", "AI citations", ChatGPT search", or "AI visibility".geo-sleuth (v2)Geolocate or chronolocate a photo with tool-verified reasoning (where was this taken / when was it taken / photo geolocation / geo-guessing). Given one or more photos, intake.py does metadata, OCR and reverse image search (Baidu/Yandex) in one command; clues and candidates go on a candidate board (board.py) that ranks them and gives the next step; clues.py looks up calling codes, driving side, plates and area codes from region packs; for satellite and street view the machine ranks first and you look only at the top few (CLIP in sat_scan.py, DINOv2+SIFT in match.py); plus sun and shadow math, OSM Overpass and DEM skylines. Every conclusion is checked against real data; output is coordinates + error radius, evidence images and tiered confidence. Use when the user shares a photo and asks where was this taken / geolocate this / when was this taken / 这是哪 / 在哪拍的 / 帮我定位这张照片 / 网络迷踪 / 图寻 / 几点拍的.AI visibilityMeasure real AI-engine visibility — traffic from ChatGPT, Perplexity, Claude, Gemini and which pages they cite — from actual GA4 referral data, not prompt sampling. Use when asked "AI visibility", "GEO/AEO check", "do LLMs cite us", "traffic from ChatGPT", or "how do we show up in AI search".AI Search SEO: Citation StrategiesUse GEO and AEO as legacy labels for AI citation readiness. For Google, the
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