Generative Engine Optimization (GEO) is the practice of improving how often a brand is mentioned or cited inside AI-generated answers, not just ranked as a webpage.

Citation Score is a metric representing how often an AI engine cites a brand’s pages as sources across a defined prompt set.

Share of Answers is the percentage of AI answers in a prompt set that mention your brand compared to competitors.

Mention Rate is the proportion of tracked prompts where an AI engine mentions your brand at least once.

Citation Rate is the proportion of tracked prompts where the AI engine provides a clickable citation to your pages.

In LLM visibility, an entity is a clearly identifiable concept—like a brand, product, person, or category—with stable attributes that AI engines can recognize.

Entity clarity is how unambiguous your brand’s category, audience, and differentiator are across your website and trusted sources.

Entity Consistency Index measures how consistently AI engines describe your brand compared to your canonical definition.

A canonical definition is the single authoritative sentence that defines what your brand is and who it serves.

Answer-first formatting places a direct, quotable answer in the first 40–80 words of a section to increase citation likelihood.

Chunking is structuring content into self-contained passages that can be extracted by AI systems without losing meaning.

A pull quote is the short excerpt an AI engine selects to summarize or justify an answer.

Evidence density is how much verifiable proof (sources, numbers, methods) exists per page section.

Proof artifacts are concrete evidence items—benchmarks, screenshots, logs, methodology—used to support claims.

A Prompt Map is a structured list of target prompts grouped by intent, used to plan content and measure GEO performance.

A prompt benchmark is a repeated test of the same prompts across engines to measure mentions and citations over time.

A competitive set is the list of brands you compare against when calculating share of answers.

Context quality measures whether AI engines describe your brand positively, neutrally, or with hesitation.

Sentiment context is how your brand is framed inside an AI answer, including the presence of praise, neutrality, or warnings.

Zero-click search occurs when users get answers without clicking any website result.

Dark chat refers to AI-driven discovery that produces little or no referral data in analytics tools, making attribution difficult.

GA4 blind spot is the inability of traditional analytics to reliably attribute AI answer influence due to missing referrers.

llms.txt is a curated text file that lists the most important pages you want AI agents to read and cite.

Structured data is machine-readable markup (often JSON-LD) that helps systems interpret your page’s meaning and metadata.

BlogPosting schema is structured data used to describe a blog article with fields like headline, author, datePublished, and mainEntityOfPage.

DefinedTerm schema describes a glossary term definition on a stable URL for machine interpretation.

Co-citation is when your brand is mentioned alongside established entities, signaling category membership and trust.

Trusted source presence is the extent to which your brand appears in places AI engines treat as credible references.

Prompt fit is how closely your page language matches the way users ask questions in AI engines.

India-first GEO adapts LLM visibility strategies to India’s multilingual queries, marketplaces, category trust dynamics, and regional intent.