Plain-English definitions for GEO, LLM SEO, AI visibility, prompt tracking and the rest of the vocabulary you'll need in the AI-first era of search.
- GEO (Generative Engine Optimization)
Optimizing your brand to be recommended inside AI-generated answers.
- GEO is the practice of optimizing brand entities, content, and citations so that LLMs like ChatGPT, Gemini and Claude mention your brand when buyers ask category-defining questions. It is the AI-era successor to SEO.
- LLM SEO
SEO tactics adapted for Large Language Models instead of traditional search engines.
- LLM SEO focuses on getting cited and surfaced inside AI answers rather than ranking blue links. Tactics include entity clarity, comparison content, community presence, and structured data.
- AI Visibility
How often your brand appears inside AI-generated answers across buyer-intent prompts.
- AI Visibility measures the presence of your brand inside answers from ChatGPT, Gemini, Claude and Perplexity for a defined prompt set. It's the AI equivalent of impression share.
- AI Share of Voice (AI SoV)
Your brand's share of total brand mentions across tracked AI answers.
- AI SoV (%) = (your brand mentions ÷ total brand mentions across all tracked prompts) × 100. The cleanest metric for benchmarking AI visibility against competitors.
- Prompt Tracking
Systematically running buyer-intent prompts across AI models to monitor brand mentions.
- Prompt tracking turns ad-hoc 'I asked ChatGPT once' checks into a repeatable measurement system. Hundreds of prompts × multiple models × weekly cadence = a real signal.
- AI Prompt Tracker
Software that runs buyer-intent prompts across LLMs and reports brand mentions.
- An AI Prompt Tracker (like ClickAI) automates running prompts across ChatGPT, Gemini and Claude, scores brand and competitor mentions, and exports the results to Excel and PDF.
- Buyer-Intent Prompt
A prompt a real prospect would type when they are close to a purchase decision.
- Examples: 'best CRM for early-stage startups', 'alternatives to HubSpot under $50/mo', 'is Notion good for engineering teams'. These are the prompts worth tracking.
- Brand Mention
An occurrence of your brand name inside an AI-generated answer.
- Detected via whole-keyword, case-insensitive matching across the full AI response. Mentions are counted per prompt and rolled up into Share of Voice.
- Citation
A source the LLM links to or quotes when generating an answer.
- Modern LLMs increasingly cite sources (Reddit, G2, YouTube, news sites). Earning a citation on a high-authority source is one of the strongest GEO levers.
- Grounding
When an LLM bases its answer on retrieved real-time or trusted sources instead of training data.
- Grounding (often via RAG or web search) improves answer accuracy and increases the importance of being present on authoritative sources.
- Hallucination
When an LLM confidently states something that isn't true.
- For brand monitoring, hallucinations matter when LLMs mis-attribute features or pricing to your brand. Prompt tracking surfaces these so you can correct them.
- Entity
A real-world thing (brand, product, person) the LLM recognizes as a distinct concept.
- Entity clarity — clean Wikidata, consistent sameAs links, structured Organization schema — directly improves how often an LLM recommends you for your category.
- Share of Answer
Percentage of tracked AI answers that mention your brand at least once.
- Different from SoV: SoA counts answers, not mentions. Useful when one answer mentions a brand many times and would otherwise skew the score.
- Multi-Model Tracking
Running the same prompt across ChatGPT, Gemini, Claude and Perplexity.
- Single-model tracking is misleading — each model answers differently. Multi-model coverage is required for a true AI visibility picture.
- AI SERP
The synthesized answer panel an AI assistant returns instead of a list of links.
- The AI SERP is the new battleground. Position #1 in Google matters less when the AI summarizes the answer before the user ever clicks.