# Prompt Metrics > Prompt Metrics helps teams measure and improve how AI assistants recommend their brand, track competitor mentions, analyze cited sources, and fix technical or content gaps that affect AI visibility. Public marketing, glossary, blog, and free-tool content for Prompt Metrics. Start with Product for the software overview, Tools for tactical diagnostics, and Glossary for definitions used throughout the site. Authenticated workspace dashboards and customer data are intentionally excluded from these files. This companion file includes inline markdown for public pages so an AI system can consume key content without visiting each page individually. ## Overview ### Homepage See what AI assistants recommend when buyers ask about your category. Find the gaps before your competitors do. Prompt Metrics is an AI visibility monitoring platform for B2B teams that need to know what buyers hear when they ask ChatGPT, Claude, Gemini, Perplexity, Grok, or DeepSeek for recommendations. ## Core promise - Be the brand AI recommends first. - See what AI assistants recommend when buyers ask about your category. Find the gaps before your competitors do. - Tracks 6 AI model families: Claude, DeepSeek, Gemini, Grok, ChatGPT, and Perplexity. - Shows visibility trends, competitor mentions, cited sources, and prompt-level evidence. ## Why teams use it - AI assistants are increasingly part of buying research, so brand visibility now depends on whether AI includes you in the shortlist. - Prompt Metrics helps product marketing, demand generation, content, SEO, and revenue teams see exactly where they are recommended or omitted. - The product is positioned as a monitoring and remediation workflow, not just a dashboard. Source: https://promptmetrics.xyz/ ## Product ### AI Monitoring Tools Track what 6 AI models say about your brand. See your visibility score, compare against competitors, and find the sources AI cites in your category. Track what 6 AI models say about your brand. See your visibility score, compare against competitors, and find the sources AI cites in your category. ## What the product tracks - **AI Visibility Score:** One number that tells you how visible your brand is across all 6 AI models, updated weekly with model-level breakdowns so you can see the trend. - **Competitor Tracking:** See who AI recommends instead of you and on which prompts. Updated weekly with side-by-side comparisons across every model. - **Citation Analysis:** AI cites specific sources when it recommends brands. See which domains drive authority in your category and which ones mention competitors but not you. - **Weekly Reports:** Every week you get the full picture: whose visibility changed, which competitors showed up in new prompts, and what you should do next. Straight to your inbox. ## Operational model - Teams define their brand, competitors, and the buying questions that matter in their category. - Prompt Metrics runs those prompts across 6 AI model families and stores the full responses. - Reports show visibility score movement, competitor coverage, and which sources AI trusts in the category. Source: https://promptmetrics.xyz/ai-monitoring-tools ### Features Deep-dive into every capability that makes AI visibility measurable, actionable, and defensible. Deep-dive into every capability that makes AI visibility measurable, actionable, and defensible. ## Key capabilities - **Visibility Score:** Your visibility score measures how often AI recommends you across every model we scan. Broken down by model, prompt cluster, and time period. Updated weekly so you see the trend, not just a snapshot. - **Prompt-Level Evidence:** No aggregate scores with no context. Prompt Metrics stores the full response text for every prompt, every model, every scan. You see exactly what AI said, word for word. That's the evidence your team needs to take action. - **Competitor Intelligence:** Add your competitors during setup. We track them alongside you across every prompt and model. See who wins which buying questions, and spot the moment someone overtakes your position — before it shows up in your pipeline numbers. - **Citation Analysis:** See every domain and article AI cites in your category. Find high-authority sources that mention competitors but not you. Track how citation influence shifts over time so you know where to invest. - **Recommendations:** Prompt Metrics turns visibility gaps into a prioritized to-do list: which review sites to get listed on, which publications to pitch, which communities to engage, and which content to create. Each recommendation includes the evidence behind it, ranked by impact. - **Site Audit:** Before AI can recommend you, it needs to crawl your site. We check your robots.txt for AI bot access, validate your JSON-LD schema, and flag technical barriers that might be keeping you invisible. No quota cost for the fast scan. - **GEO Optimization:** AI models favor content with authoritative tone, citations, expert quotes, and specific data. Our GEO analysis scores your pages across 8 dimensions and tells you exactly what to rewrite for better AI visibility. Available on Pro and Business. - **Multi-Model Coverage:** Claude, DeepSeek, Gemini, Grok, ChatGPT, and Perplexity. Each model recommends differently — different sources, different favorites, different reasoning. We cover all of them so you don't optimize for one and miss the others. ## What makes the product different - Multi-model coverage instead of focusing on a single assistant. - Prompt-level evidence instead of black-box aggregate scores. - Recommendations tied to cited sources, crawlability, and content gaps. Source: https://promptmetrics.xyz/features ### How It Works Tell us your brand and competitors. We query 6 AI models with your prompts and show you what comes back. Tell us your brand and competitors. We query 6 AI models with your prompts and show you what comes back. ## Workflow 1. **Set up your brand** (2 minutes): Enter your brand name and website. Add competitors. Write the buying questions your customers actually ask — or grab from our suggestions. Two minutes, tops. 2. **We scan every AI model** (6 models): We query ChatGPT, Claude, Gemini, Perplexity, Grok, and DeepSeek with your prompts. Every response stored in full. You see who got mentioned, who didn't, and what sources each model pulled. 3. **See your visibility report** (Instant): One score across all 6 models. Drill into any prompt to see which competitors beat you and what sources got cited. We tell you what to fix first. ## Practical takeaway - Setup is positioned as a lightweight workflow for marketing teams, not an engineering integration. - The product continuously monitors how AI assistants describe the brand and who they recommend instead. Source: https://promptmetrics.xyz/how-it-works ### Pricing Pick a plan that fits your team. Every plan includes a free trial. Pick a plan that fits your team. Every plan includes a free trial. ## Plans ### Starter - Price: $29/month - One workspace for your team. See what AI says about you and start fixing it. - Best for: validating that AI visibility matters for your pipeline before scaling. - Includes: 1 workspace, 2,500 responses per month, 3 members per workspace, 25 prompts per workspace, Prompt-level response records, Citation source tracking, Site audit (technical barriers), Recommendation backlog. ### Pro - Price: $49/month - Bigger limits, more workspaces. For teams that check AI visibility the way they check pipeline: every week. - Best for: growth teams running AI visibility as part of their regular GTM cadence. - Includes: 2 workspaces, 5,000 responses per month, Unlimited members per workspace, 50 prompts per workspace, Prompt-level response records, Citation source tracking, Source enrichment & contact info, GEO content optimization, Site audit (technical barriers), Daily AI insights, Weekly email reports, Recommendation backlog. ### Business - Price: $149/month - Everything in Pro, scaled for multiple brands and larger teams. Priority support included. - Best for: multi-brand organizations standardizing AI visibility across teams and regions. - Includes: 5 workspaces, 15,000 responses per month, Unlimited members per workspace, 150 prompts per workspace, Prompt-level response records, Citation source tracking, Source enrichment & contact info, GEO content optimization, Site audit (technical barriers), Daily AI insights, Weekly email reports, Geographic visibility breakdowns, Priority support, Recommendation backlog. ## Commercial notes - Every plan includes a 7-day free trial. - Plans differ mainly on response volume, workspace limits, prompt limits, collaboration, GEO features, and geographic breakdowns. Source: https://promptmetrics.xyz/pricing ### FAQ Direct answers on fit, implementation, and plan boundaries. Direct answers on fit, implementation, and plan boundaries. ## Frequently asked questions ### What AI assistants do you monitor? We monitor Claude, DeepSeek, Gemini, Grok, ChatGPT, and Perplexity. We add new ones as they get popular enough to matter. ### How does the visibility score work? Your visibility score is how often your brand shows up when AI answers questions in your market. We calculate it from actual AI responses across every model we monitor. No estimates. ### Can I track competitors? Yes. Add competitors during setup and we track them next to yours. You'll see who gets mentioned more, who ranks higher, and which sources cite them but not you. ### How often is data refreshed? Scans run on your schedule. Most people do weekly. You can also kick off a manual scan whenever you want and watch it run. ### What happens after the free trial? After 7 days, your trial converts to a paid plan. Cancel anytime. Your data stays available if you come back. ### Is my data private? Yes. Your prompts, competitors, and visibility data stay private to your workspace. We don't share or sell any of it. ### Can't I just manually check AI responses? Sure. Ask ChatGPT once a week and screenshot it. But AI responses change by model, by phrasing, and by week. One manual check is an anecdote. We run the same prompts across 6 models every week and store the full text, so you can actually compare and act on it. ### How is this different from brand monitoring? Brand monitoring tracks mentions on websites, social media, and news. We track what AI assistants actually say when someone asks for recommendations in your category. Different channel, different data. And for a lot of companies, the one that's starting to drive more pipeline. ### Is AI visibility actually affecting our pipeline? If your buyers ask AI for recommendations before they talk to sales, then AI is shaping shortlists before you even get a conversation. You can ignore that, but not cheaply. This is the channel that's replacing Google for buying research, and right now you probably have zero data on it. Source: https://promptmetrics.xyz/faq ## Tools ### Tools Free AI visibility tools to diagnose whether AI crawlers can find and cite your site. Spot technical blockers before they cost you mentions. Free AI visibility tools to diagnose whether AI crawlers can find and cite your site. Spot technical blockers before they cost you mentions. ## Available free tools - **AI Crawlability Checker:** Free AI crawlability checker — see whether GPTBot, ClaudeBot, and other AI crawlers can reach your page. Check robots directives, schema, and content structure. - **Free llms.txt Generator:** llms.txt is a Markdown file at your site root that helps AI models understand your content. This free generator crawls your sitemap and creates spec-compliant llms.txt and llms-full.txt files. ## How the tools fit the product - The tools are top-of-funnel diagnostics for AI visibility and crawlability. - They are meant to help users validate problems before moving to ongoing monitoring. Source: https://promptmetrics.xyz/tools ### AI Crawlability Checker Free AI crawlability checker — see whether GPTBot, ClaudeBot, and other AI crawlers can reach your page. Check robots directives, schema, and content structure. Checks whether AI crawlers can reach and understand a public page. ## What it tests - robots.txt access and blocking directives - Meta robots tags and X-Robots-Tag headers - Schema and FAQ markup - Heading hierarchy, image alt text, and page size ## Intended outcome - Identify the technical barriers that prevent AI systems from crawling or understanding a page. - Prioritize fixes before moving on to content optimization or citation-building work. Source: https://promptmetrics.xyz/tools/ai-crawlability-checker ### llms.txt Generator llms.txt is a Markdown file at your site root that helps AI models understand your content. This free generator crawls your sitemap and creates spec-compliant llms.txt and llms-full.txt files. llms.txt is a plaintext Markdown file hosted at a website's root (e.g. example.com/llms.txt) that provides AI language models with a structured, machine-readable summary of the site — including page titles, URLs, and short descriptions. It is the AI equivalent of robots.txt: where robots.txt tells search crawlers what to access, llms.txt tells AI reasoning systems what a site is about and which pages matter most. This tool generates spec-compliant `llms.txt` and `llms-full.txt` files for any public site by discovering pages, extracting titles and descriptions, and formatting them for AI consumption. ## Workflow 1. Enter a public domain or homepage URL. 2. The tool discovers pages from the sitemap or navigation. 3. It generates a concise `llms.txt` and a richer `llms-full.txt` with inline markdown content. ## llms.txt vs robots.txt vs sitemap.xml - **llms.txt** targets AI language models; provides page titles, URLs, and descriptions in Markdown; hosted at /llms.txt; adopted since 2024. - **robots.txt** targets search engine crawlers; controls crawl access with allow/disallow directives; hosted at /robots.txt; adopted since 1994. - **sitemap.xml** targets search engine indexers; lists all URLs with change frequency and priority metadata; hosted at /sitemap.xml; adopted since 2005. ## Intended outcome - Help site owners publish a root-level AI-readable site summary that improves AI visibility and citation accuracy. - Reduce the amount of crawling needed for models that support the emerging llms.txt convention. Source: https://promptmetrics.xyz/tools/llms-txt-generator ## Glossary ### AI Visibility Glossary Plain-language definitions for AI visibility, mention rates, GEO, RAG, citation analysis, and more. Built for marketers and growth teams. Plain-language definitions for AI visibility, mention rates, GEO, RAG, citation analysis, and more. Built for marketers and growth teams. ## Coverage - 40 public glossary terms. - Categories: metrics, strategy, technical concepts, and foundational AI concepts. - Built for marketers, founders, and growth teams learning how AI search and AI recommendations work. ## Representative topics - [AI Visibility Score](https://promptmetrics.xyz/glossary/ai-visibility-score): An AI visibility score measures how often your brand shows up when someone asks an AI assistant a buying question. It rolls up mention frequency, position in the response, and sentiment into one number across multiple models. - [AI Mention Rate](https://promptmetrics.xyz/glossary/ai-mention-rate): AI mention rate is the percentage of AI responses that include a specific brand name when queried with category-relevant prompts. Ask "what's the best CRM for startups" a hundred times across models, count how many responses name your brand. That's mention rate. - [AI Citation](https://promptmetrics.xyz/glossary/ai-citation): An AI citation is a reference to a specific domain, article, or source that an AI assistant includes in its response to back up a recommendation or claim. Citations reveal which sources AI models treat as authoritative in a given category. - [Generative Engine Optimization (GEO)](https://promptmetrics.xyz/glossary/generative-engine-optimization): Generative Engine Optimization (GEO) is the practice of optimizing content so that AI search engines and assistants are more likely to surface, cite, and recommend it. First formalized in a [research paper from Princeton and IIT Delhi](https://arxiv.org/abs/2311.09735), GEO is SEO adapted for the era of AI-generated answers. - [AI Brand Monitoring](https://promptmetrics.xyz/glossary/ai-brand-monitoring): AI brand monitoring is the ongoing practice of tracking what AI assistants say about a brand across multiple models and prompt variations. It gives you ongoing visibility into how AI recommends, describes, and positions your brand relative to competitors. - [AI Crawler](https://promptmetrics.xyz/glossary/ai-crawler): An AI crawler is a bot deployed by AI companies to scan, index, and ingest web content for training data or real-time retrieval. GPTBot, ClaudeBot, Google-Extended: these are how AI models discover and access your content. - [Structured Data for AI](https://promptmetrics.xyz/glossary/structured-data-for-ai): Structured data for AI refers to machine-readable markup (usually JSON-LD with schema.org vocabulary) that helps AI models understand the entities, relationships, and facts on a web page. Good structured data increases the odds that AI models correctly interpret and cite your content. - [AI Source Authority](https://promptmetrics.xyz/glossary/ai-source-authority): AI source authority is the trustworthiness and influence that AI models assign to specific domains, publications, and content sources when generating recommendations. Sources with high authority get cited more often and weighted more heavily in AI responses. - [Retrieval-Augmented Generation (RAG)](https://promptmetrics.xyz/glossary/retrieval-augmented-generation): Retrieval-Augmented Generation (RAG) is a technique where AI models pull in relevant documents or data from external sources before generating a response. It lets AI access current, specific information beyond its training data and cite where it came from. - [AI Search Intent](https://promptmetrics.xyz/glossary/ai-search-intent): AI search intent is the user's underlying goal when asking an AI assistant a question. It bridges traditional keyword intent analysis into the AI layer. Same intent categories (informational, navigational, commercial, transactional), but the response format and competitive dynamics are very different. Source: https://promptmetrics.xyz/glossary ### AI Visibility Score What an AI visibility score is, how it's calculated, and why it matters for brand monitoring. Track yours across ChatGPT, Claude, Gemini, and Perplexity. An AI visibility score measures how often your brand shows up when someone asks an AI assistant a buying question. It rolls up mention frequency, position in the response, and sentiment into one number across multiple models. ## Why this metric exists SEO metrics tell you where you rank on Google. An AI visibility score tells you what happens when buyers skip Google entirely and ask AI directly. Brands that score low here are invisible to a growing share of buying decisions, regardless of how well their traditional marketing performs. The numbers back this up: - ChatGPT has [800M+ weekly active users](https://openai.com/index/spring-update/) - Perplexity processes [millions of search queries daily](https://www.demandsage.com/perplexity-ai-statistics/) - Google AI Overviews appear on a growing percentage of search results Without measurement here, you have no idea what a fast-growing segment of buyers is hearing about you. ## How scoring works Multiple AI models get queried with prompts that mirror real buyer questions in your category. Each response is analyzed for: - Brand mentions: does the model name your brand? - Position: are you mentioned first, in the middle, or as an afterthought? - Sentiment: is the mention positive, neutral, or cautionary? - Citation sources: what domains does the model reference? The results roll up into a composite score that tracks over time. [Prompt Metrics](https://promptmetrics.xyz/ai-monitoring-tools) runs these scans across all major AI models automatically. ## What moves the score Earning citations from sources AI models trust, creating content that directly answers category questions, making sure your site is crawlable by [AI bots](https://promptmetrics.xyz/glossary/ai-crawler), and getting listed on review platforms and industry publications that AI models reference. The most impactful levers: - Publish on cited domains: guest posts, PR placements, industry publications - Create authoritative content: data-rich, expert-attributed, well-structured - Use [structured data](https://promptmetrics.xyz/glossary/structured-data-for-ai): JSON-LD markup AI models can parse - Build review presence: [G2](https://www.g2.com), [Capterra](https://www.capterra.com), and similar platforms AI models trust No single lever is enough on its own. ## Frequently Asked Questions ### How is an AI visibility score calculated? Each AI model gets queried with category-relevant prompts. The results are scored for mention frequency, position in the response, and sentiment, then normalized to a 0-100 scale. The final score is a weighted composite across all models tested. [Prompt Metrics](https://promptmetrics.xyz/features) automates this across ChatGPT, Claude, Gemini, and Perplexity. ### How often should I check my AI visibility score? Weekly. AI model outputs shift as training data updates, and a weekly cadence gives you real trends without noise. More frequent checks rarely add useful signal. [Set up automated weekly scans](https://promptmetrics.xyz/pricing) to track changes over time. ### What is a good AI visibility score? It depends on your category. In competitive markets, a score above 60 puts you in strong territory. Category leaders often score 75+ on their core prompt clusters. The number matters less than the trend. Consistent improvement means your [GEO strategy](https://promptmetrics.xyz/glossary/generative-engine-optimization) is working. ### How does AI visibility score differ from SEO rankings? SEO rankings tell you where you appear on Google. An AI visibility score tells you what happens when buyers skip Google entirely and ask AI directly. The signals and optimization levers are completely different. Brands increasingly need both. Source: https://promptmetrics.xyz/glossary/ai-visibility-score ### AI Mention Rate AI mention rate measures the percentage of AI responses that reference your brand. Learn how to track it across ChatGPT, Claude, and Gemini with Prompt Metrics. AI mention rate is the percentage of AI responses that include a specific brand name when queried with category-relevant prompts. Ask "what's the best CRM for startups" a hundred times across models, count how many responses name your brand. That's mention rate. ## What it tells you Mention rate is the most direct signal of whether AI assistants know your brand exists in your category. A low rate means buyers using AI for research will never encounter you. It separates AI-visible brands from invisible ones, and it doesn't care how much you spend on Google Ads. Rough benchmarks: - 0-10%: Invisible to AI. You have work to do. - 10-30%: Recognized but not preferred. Room to grow. - 30-50%: Competitive. Focus on maintaining and improving. - 50%+: Category leader in AI visibility. ## Differences across models Your mention rate on ChatGPT may look nothing like your rate on Claude or Gemini. Each model has different training data and different recommendation patterns. Measuring across all major models shows you where you have strength and where you have gaps. | Model | Training approach | Update frequency | |-------|------------------|------------------| | [ChatGPT](https://chatgpt.com) | Broad web corpus + browsing | Regular updates | | [Claude](https://claude.com) | Curated training data | Periodic updates | | [Gemini](https://gemini.google.com) | Google's web index | Near real-time | | [Perplexity](https://www.perplexity.ai) | Live web retrieval ([RAG](https://promptmetrics.xyz/glossary/retrieval-augmented-generation)) | Real-time | ## Turning data into action A low mention rate points to specific gaps. You might be missing from sources AI models trust, absent from review platforms, or lacking content that answers the exact questions buyers ask AI. Each gap maps to a concrete action: - Missing from cited sources? Publish on domains AI models reference. - Absent from reviews? Get listed on [G2](https://www.g2.com), [Capterra](https://www.capterra.com), or category-specific platforms. - Weak content coverage? Create content targeting high-intent prompts. - Inconsistent positioning? Align messaging across all web properties. [Prompt Metrics](https://promptmetrics.xyz/features) identifies exactly which prompts and models are gaps. ## Frequently Asked Questions ### What is a good AI mention rate? Depends on your category. In a crowded space, 30%+ across major models is strong. Category leaders often hit 50%+ on specific prompt clusters. In niche categories with fewer competitors, you should expect higher. ### How does AI mention rate differ from brand mention monitoring? Traditional brand monitoring tracks mentions across web, social, and news. AI mention rate specifically measures how often AI assistants name your brand when answering buyer questions, a completely separate channel with its own levers. See [AI brand monitoring](https://promptmetrics.xyz/glossary/ai-brand-monitoring) for the broader practice. ### Does mention rate vary across AI models? A lot. Your mention rate on ChatGPT may be 40% while Claude shows 15% and Gemini shows 60%. Each model has different training data, different recommendation patterns, and different update cycles. [Track all models](https://promptmetrics.xyz/features) to get the complete picture. ### How can I improve my AI mention rate? Focus on the sources AI models trust in your category. Publish authoritative content, earn citations on high-trust domains, get listed on review platforms, and create content that directly answers the prompts buyers use. Track changes weekly to see what works. Source: https://promptmetrics.xyz/glossary/ai-mention-rate ### AI Citation What AI citations are, why they matter for visibility, and how to earn them from ChatGPT, Claude, and Gemini. Track citation sources with Prompt Metrics. An AI citation is a reference to a specific domain, article, or source that an AI assistant includes in its response to back up a recommendation or claim. Citations reveal which sources AI models treat as authoritative in a given category. ## Why citations matter When an AI model cites your domain, it treats your content as worth referencing. Cited sources get more visibility, which drives more traffic, which reinforces authority. Brands that earn citations consistently tend to dominate AI recommendations in their category. The citation flywheel: 1. Authoritative content gets cited by AI models 2. AI citations drive traffic and brand awareness 3. Increased authority leads to more citations 4. Repeat. It compounds. ## How each model cites differently Each AI model handles citations differently, and understanding these patterns helps you optimize for each: - Perplexity provides inline citations with clickable URLs. Most transparent about sources. - Gemini references sources in context, sometimes with links. Benefits from Google's web index. - ChatGPT may reference domains or publications without explicit links. Browsing mode adds real-time citations. - Claude references domains and publications. More conservative about specific claims. Understanding these differences helps you optimize for each model's [citation behavior](https://promptmetrics.xyz/glossary/ai-source-authority). ## Building a citation strategy Start by identifying which domains AI models already trust in your category. Then build your presence step by step: - Guest posts and PR: publish on the domains AI models cite in your space - Review platforms: get listed and reviewed on G2, Capterra, TrustRadius - Expert content: create data-rich, expert-attributed articles with specific data points - Structured markup: use [JSON-LD schema](https://promptmetrics.xyz/glossary/structured-data-for-ai) so AI can extract facts cleanly - Consistency: keep brand information accurate across all web properties [Prompt Metrics](https://promptmetrics.xyz/features) shows you exactly which sources AI models cite in your category, so you can focus your efforts. ## Frequently Asked Questions ### Do all AI models provide citations? Not equally. [Perplexity](https://www.perplexity.ai) cites sources inline with URLs. Gemini references sources in context. ChatGPT and Claude may reference domains or publications without explicit links. Tracking citations across models reveals which sources carry the most weight. ### How can I get my site cited by AI? Create well-structured content that directly answers category questions. Make sure [AI crawlers](https://promptmetrics.xyz/glossary/ai-crawler) can access your site, use [structured data markup](https://promptmetrics.xyz/glossary/structured-data-for-ai), and build presence on domains that AI models already trust in your space. ### Are AI citations the same as backlinks? No. Backlinks are hyperlinks from one website to another and matter for SEO. AI citations are references AI models include in their responses. They influence brand perception and buyer decisions rather than search rankings. Both matter, but they work through different mechanisms. ### Which domains get cited most by AI models? Industry publications, established review platforms ([G2](https://www.g2.com), [Capterra](https://www.capterra.com), [TrustRadius](https://www.trustradius.com)), recognized media outlets, official documentation, and community platforms like [Reddit](https://www.reddit.com) and [Stack Overflow](https://stackoverflow.com/questions). The specific mix varies by category. [Track citation sources](https://promptmetrics.xyz/features) in your space to build a targeted strategy. Source: https://promptmetrics.xyz/glossary/ai-citation ### Generative Engine Optimization (GEO) What is Generative Engine Optimization (GEO)? How to optimize content for AI search engines like ChatGPT and Perplexity. Measure your GEO progress with Prompt Metrics. Generative Engine Optimization (GEO) is the practice of optimizing content so that AI search engines and assistants are more likely to surface, cite, and recommend it. First formalized in a [research paper from Princeton and IIT Delhi](https://arxiv.org/abs/2311.09735), GEO is SEO adapted for the era of AI-generated answers. ## From links to answers Traditional search returns links. AI search returns answers. Instead of competing for page-one rankings, brands now compete for inclusion in AI-generated responses. The distinction matters: - Google search: "Here are 10 links, figure it out" - [AI search](https://promptmetrics.xyz/glossary/ai-search): "Here's the answer, with these brands recommended" With [Google AI Overviews](https://blog.google/products-and-platforms/products/search/generative-ai-google-search-may-2024/) now appearing on a growing share of search results, even traditional search is becoming AI-mediated. GEO is the discipline that addresses this shift. ## What GEO focuses on GEO targets the signals AI models use to select and cite content: - Authoritative tone: specific claims backed by data, not vague marketing speak - Expert credentials: named experts, credentials, and quotes that signal domain expertise - Structured content: clear headings, [schema markup](https://promptmetrics.xyz/glossary/structured-data-for-ai), machine-readable formatting - Trusted domain presence: content on sites AI models already cite in your category - Full coverage: thorough answers to the questions buyers ask These signals differ from traditional SEO ranking factors. Keyword density and backlink counts are less important than content authority and structural clarity. ## Getting started A practical GEO roadmap: 1. Audit your AI visibility with [Prompt Metrics](https://promptmetrics.xyz/features) to measure your current [visibility score](https://promptmetrics.xyz/glossary/ai-visibility-score) and [mention rate](https://promptmetrics.xyz/glossary/ai-mention-rate) 2. Identify high-value prompts: what questions do your buyers ask AI? Which ones generate competitor mentions? 3. Map citation sources: which domains do AI models trust in your space? 4. Optimize existing content: add data, expert attribution, and [structured data](https://promptmetrics.xyz/glossary/structured-data-for-ai) to your best pages 5. Publish an [llms.txt file](https://promptmetrics.xyz/tools/llms-txt-generator) so AI systems can quickly understand your highest-value pages 6. Build external presence: publish on cited domains, earn reviews, contribute to authoritative publications 7. Monitor and iterate: track weekly to see what moves the needle Early movers have a 6-12 month head start. That gap widens every month. ## Frequently Asked Questions ### How is GEO different from SEO? SEO optimizes for ranking in traditional search results. GEO optimizes for being cited or recommended in AI-generated responses. The signals are different: AI models weight authoritative tone, specific data, expert quotes, and structured content more than keyword density and backlink profiles. ### Is GEO replacing SEO? No. Traditional search still drives real traffic. But as more users turn to AI for research, brands need both. Many GEO improvements (better content quality, [structured data](https://promptmetrics.xyz/glossary/structured-data-for-ai)) also help SEO, so the work pays off in both channels. ### What are the first steps for GEO? Start with three questions: Which AI models matter for your category? What prompts do your buyers actually use? Which [sources do AI models trust](https://promptmetrics.xyz/glossary/ai-source-authority) in your space? From there, optimize existing content for AI readability and build presence on the domains AI models cite. ### How do I measure GEO success? Track your [AI visibility score](https://promptmetrics.xyz/glossary/ai-visibility-score) and [mention rate](https://promptmetrics.xyz/glossary/ai-mention-rate) across models over time. Monitor which [sources get cited](https://promptmetrics.xyz/glossary/ai-citation) in your category. [Prompt Metrics](https://promptmetrics.xyz/features) provides automated tracking across all major AI platforms. Source: https://promptmetrics.xyz/glossary/generative-engine-optimization ### AI Brand Monitoring What is AI brand monitoring? How to track what ChatGPT, Claude, and Gemini say about your brand. See how Prompt Metrics helps you monitor it consistently. AI brand monitoring is the ongoing practice of tracking what AI assistants say about a brand across multiple models and prompt variations. It gives you ongoing visibility into how AI recommends, describes, and positions your brand relative to competitors. ## The blind spot Most brands monitor their web presence, social mentions, and review scores. Almost none monitor what AI assistants say about them. This is a growing blind spot: - Buyers increasingly use AI for product research - AI recommendations shape shortlists before a sales conversation happens - AI descriptions of your brand are invisible without dedicated monitoring - A single unfavorable AI characterization repeats across millions of conversations Without monitoring, you have zero idea what a fast-growing discovery channel is telling buyers about you. ## What to track Effective AI brand monitoring covers multiple dimensions: - Mention frequency: how often AI names your brand across models and prompts - Competitive positioning: are you mentioned first, last, or not at all? - Sentiment and context: positive recommendation or cautionary mention? - [Citation sources](https://promptmetrics.xyz/glossary/ai-citation): which domains does AI reference in your category? - Changes over time: is your visibility trending up or down? - Model-specific patterns: where are you strong (ChatGPT) vs. weak (Claude)? Each dimension tells you something different about your AI brand presence. ## Setting up a monitoring program A structured approach to AI brand monitoring: 1. Define your prompts: the questions your buyers actually ask AI (start with sales team input) 2. Select your models: [ChatGPT](https://chatgpt.com), [Claude](https://claude.com), [Gemini](https://gemini.google.com), and [Perplexity](https://www.perplexity.ai) at minimum 3. Establish cadence: weekly scans give the best signal-to-noise ratio 4. Track competitors: monitor your competitive set alongside your own brand 5. Set up alerts: flag significant changes in mention rate or sentiment 6. Create action workflows: route insights to content, marketing, and product teams Steps 1-5 can be automated (that's what [Prompt Metrics](https://promptmetrics.xyz/features) does). Step 6 is where your team earns its keep. ## Frequently Asked Questions ### Why can't I just manually check what AI says about my brand? Manual checks give you anecdotes, not data. AI responses vary by model, prompt phrasing, time, and context. Systematic monitoring across multiple models and prompts gives you consistent, comparable data you can actually make decisions with. ### How often should I monitor my AI brand presence? Weekly. AI model outputs can shift with training updates, so less frequent monitoring risks missing changes. More frequent monitoring rarely adds signal worth acting on. [Prompt Metrics](https://promptmetrics.xyz/pricing) offers automated weekly scanning across all major models. ### What should I monitor beyond my own brand? Your top 5-10 competitors. AI recommendations are inherently comparative. Models recommend brands relative to alternatives. Monitoring only yourself means missing half the picture. Track competitor [share of voice](https://promptmetrics.xyz/glossary/ai-share-of-voice) alongside your own. ### How quickly can I see results from AI brand monitoring? You'll get useful data from your first scan. Trends become visible after 3-4 weeks of consistent monitoring. Most teams find their first blind spot, an AI model saying something unexpected about their brand, within the first week. Source: https://promptmetrics.xyz/glossary/ai-brand-monitoring ### AI Crawler What are AI crawlers? GPTBot, ClaudeBot, and other AI bots that index your content. Learn why your robots.txt matters and how it affects your AI visibility. An AI crawler is a bot deployed by AI companies to scan, index, and ingest web content for training data or real-time retrieval. GPTBot, ClaudeBot, Google-Extended: these are how AI models discover and access your content. ## How they work AI crawlers function like search engine crawlers but serve different purposes. They scan web pages to collect content for: - Pre-training data: building the model's base knowledge - Fine-tuning: improving model performance on specific domains - Real-time retrieval: [RAG systems](https://promptmetrics.xyz/glossary/retrieval-augmented-generation) that fetch current content Each AI company runs its own crawler with its own behavior, rate limits, and `robots.txt` directives: | Crawler | Company | Purpose | |---------|---------|--------| | [GPTBot](https://developers.openai.com/api/docs/bots) | OpenAI | Training + browsing | | [ClaudeBot](https://support.claude.com/en/articles/8896518-does-anthropic-crawl-data-from-the-web-and-how-can-site-owners-block-the-crawler) | Anthropic | Training data | | [Google-Extended](https://developers.google.com/crawling/docs/crawlers-fetchers/overview-google-crawlers) | Google | AI features (Gemini, AI Overviews) | | PerplexityBot | Perplexity | Real-time search retrieval | ## Robots.txt and AI access Your [`robots.txt`](https://developers.google.com/search/docs/crawling-indexing/robots/intro) controls which AI crawlers can reach your content. Many sites accidentally block AI crawlers through: - Overly restrictive blanket rules (`User-agent: * / Disallow: /`) - Not explicitly allowing newer AI bots - CMS default settings that block unknown crawlers - CDN or firewall rules that rate-limit or block bot traffic A site audit should verify that major AI crawlers have access to your content pages. This is table stakes. If crawlers can't reach your content, nothing else in your [GEO strategy](https://promptmetrics.xyz/glossary/generative-engine-optimization) matters. ## Making your content crawl-friendly Beyond allowing access, optimize for AI crawler efficiency: - Clean HTML structure: semantic elements (`
`, `
`, `

`-`

`) - [Schema.org markup](https://promptmetrics.xyz/glossary/structured-data-for-ai): JSON-LD structured data AI systems can parse directly - Fast page loads: slow pages may not get fully crawled - Clear content hierarchy: headings and sections that map to topic structure - Minimal JavaScript rendering: content available in initial HTML, not behind JS execution - XML sitemap: include all content pages you want AI to discover - `llms.txt`: publish a structured summary of key pages with the [llms.txt generator](https://promptmetrics.xyz/tools/llms-txt-generator) These technical basics make your content easier for AI systems to parse, understand, and reference in their responses. [Prompt Metrics](https://promptmetrics.xyz/features) tracks whether AI models are actually discovering and citing your content. ## Frequently Asked Questions ### Should I block AI crawlers? Blocking them prevents your content from being used in AI training and retrieval, which means AI models won't reference or recommend your brand. For most businesses seeking AI visibility, you want them crawling your site. Block only if you have specific IP protection concerns. ### Which AI crawlers should I allow? [GPTBot](https://developers.openai.com/api/docs/bots) (OpenAI), [ClaudeBot](https://support.claude.com/en/articles/8896518-does-anthropic-crawl-data-from-the-web-and-how-can-site-owners-block-the-crawler) (Anthropic), [Google-Extended](https://developers.google.com/crawling/docs/crawlers-fetchers/overview-google-crawlers) (Google AI), and PerplexityBot (Perplexity) are the main ones. Allow all of them unless you have a specific reason not to. Check your [`robots.txt`](https://developers.google.com/search/docs/crawling-indexing/robots/intro) to make sure none are inadvertently blocked. ### How do I check if AI crawlers can access my site? Check your `robots.txt` file for `User-agent: GPTBot`, `User-agent: ClaudeBot`, `User-agent: Google-Extended`, and `User-agent: PerplexityBot`. If any are set to `Disallow: /`, those crawlers can't access your content. Also check for overly broad `Disallow` rules that might block them unintentionally. ### Do AI crawlers respect robots.txt? The major AI companies (OpenAI, Anthropic, Google, and Perplexity) have committed to respecting `robots.txt` directives. However, enforcement varies and smaller AI companies may not always comply. [Structured data](https://promptmetrics.xyz/glossary/structured-data-for-ai) provides an additional layer of control over how your content is used. Source: https://promptmetrics.xyz/glossary/ai-crawler ### Structured Data for AI How structured data and JSON-LD schema markup improve your content's discoverability by AI models like ChatGPT and Gemini. Learn how it boosts AI visibility. Structured data for AI refers to machine-readable markup (usually JSON-LD with schema.org vocabulary) that helps AI models understand the entities, relationships, and facts on a web page. Good structured data increases the odds that AI models correctly interpret and cite your content. ## Why it matters for AI AI models process vast amounts of unstructured text, but structured data gives them explicit, machine-readable signals about what a page contains. The difference is real: - Without structured data: AI must infer product features, pricing, and relationships from prose - With structured data: AI gets explicit facts it can extract and attribute with confidence A page with Product schema including features, pricing, and reviews is far easier for a model to extract from than parsing the same data from paragraphs of marketing copy. ## Schema types worth implementing Priority schema types for AI visibility, ordered by impact: 1. Organization: brand identity, logo, contact info, social profiles 2. Product: offerings, features, pricing, availability 3. Article: authoritative content with author, date, publisher 4. FAQPage: question-answer pairs AI can directly use in responses 5. HowTo: procedural content AI models reference for instructions 6. Review/AggregateRating: social proof signals AI models weight Implementing these correctly gives AI models structured facts to work with, rather than requiring them to extract information from unstructured text. The more accurate and complete your markup, the more confidently AI can cite your content. ## Getting it right Best practices for structured data that helps AI visibility: - Use JSON-LD format, preferred by Google and most AI systems - Accuracy is critical. Markup must reflect visible page content. Inaccurate markup hurts. - Be thorough. Include as much detail as possible within each schema type. - Validate regularly. Use testing tools after any page changes. - Keep it updated. Stale structured data (old pricing, discontinued features) misleads AI. - Nest appropriately. Use `@graph` to connect related entities on the same page. Structured data is a building block for AI visibility. Combined with [quality content](https://promptmetrics.xyz/glossary/ai-content-optimization) and [source authority](https://promptmetrics.xyz/glossary/ai-source-authority), it creates the conditions for consistent AI citation. [Prompt Metrics](https://promptmetrics.xyz/features) shows you how AI models interact with your structured data. ## Frequently Asked Questions ### What types of structured data help with AI visibility? Organization, Product, Article, FAQ, HowTo, and Review schema types matter most. They help AI models identify your brand, understand your offerings, and extract factual claims. The key is accurate, complete markup that reflects what the page actually says. ### Does structured data guarantee AI citation? No. It improves the probability but doesn't guarantee anything. AI models use multiple signals including content quality, [source authority](https://promptmetrics.xyz/glossary/ai-source-authority), and topical relevance. Structured data makes your content easier to parse. Necessary, but not sufficient. ### What format should I use for structured data? JSON-LD is the preferred format. It's recommended by Google and most easily parsed by AI systems. Embed it in `