An evidence-led reference to the concepts, metrics and mechanisms that determine how brands are discovered, cited and recommended by AI systems. Each entry establishes a precise definition, explains how the concept is measured or how it works, and connects it to the methods and research behind it.
Scope
The Index covers AI-driven search and answer surfaces: ChatGPT search, Perplexity, Google AI Overviews and AI Mode, Gemini, and Claude. Entries are written and maintained by Harpal Singh and reviewed on a rolling basis, with a visible last-reviewed date on every page. Metrics defined by blimpp are labelled as such and published with their measurement method; no proprietary term is presented as an established industry standard.
Featured concepts
- Citation Probability
- Citation Volatility
- Recommendation Share
- AI Share of Voice
- AI Visibility
- Query Fan-Out
- Source Influence
- Reddit Influence
- ChatGPT Search
- Generative Engine Optimisation
Browse by category
Measurement. The metrics used to quantify AI visibility: AI visibility, citation probability, citation volatility, recommendation share, AI share of voice, brand mention probability, prompt coverage, cross-engine citation overlap.
Retrieval. How systems discover and select information: query fan-out, grounding, retrieval-augmented generation, passage retrieval, source selection, citation absorption, extractability.
Entities. Identity and association signals: entity association, entity salience, brand disambiguation, attribute association, entity home, knowledge graph presence.
Authority. External evidence and consensus: source influence, third-party consensus, Reddit influence, publisher influence, earned authority, review influence, information gain.
Platforms. Stable references for the major AI search surfaces: ChatGPT search, Perplexity, Gemini, Google AI Mode, Google AI Overviews, Claude, and the AI search crawlers.
Optimisation. Industry practice and intervention types: AI SEO, generative engine optimisation, answer engine optimisation, retrieval optimisation, citation engineering, AI reputation management, llms.txt.
Methodology and research
Every metric in the Index links to a methodology page describing how it is measured, including sampling, repetition and recording conditions. Start with the repeated-run protocol and how citations, mentions and recommendations are measured. The AI Search Research programme sets out the studies planned against these methods and the evidence standards they follow.
About the Index
The Index is published by blimpp and edited by Harpal Singh. Entries are corrected openly: material changes update the visible review date, and corrections are noted on the page they affect. Definitions are written to be self-contained and non-promotional so they can be referenced accurately by other writers and systems. To see how your own brand currently appears across AI engines, you can run a free check at leoseo.com.
Editor: Harpal Singh · Last reviewed: 7 August 2026