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GEO and AI search glossary

The vocabulary of generative engine optimization borrows from search, from information retrieval and from machine learning, which makes it easy to talk past each other. These are the terms EGNITE uses, defined the way we use them in reports.

Key takeaways

  • Citation share is the closest GEO equivalent to a ranking.
  • Extractability describes whether a passage can be lifted verbatim.
  • Grounding is why some engines cite sources and others do not.

Terms

Generative Engine Optimization (GEO)
The practice of structuring content, data and entity signals so generative AI engines can read, trust and cite a company inside the answers they generate.
Answer engine
A system that returns a synthesised answer rather than a list of links — ChatGPT, Perplexity, Gemini, Claude, Copilot, DeepSeek and Google's AI answers.
Citation share
The percentage of tracked answers, for a fixed prompt set, in which a given company is named. The nearest GEO equivalent of a keyword ranking.
Answer position
Where a company appears within a generated answer. Being named first carries materially more weight than being named last.
Prompt set
The fixed list of buyer questions tracked over time. Stability of the set is what makes trends comparable week to week.
Extractability
Whether a passage states a complete, self-contained fact that a model can quote without reformulation. Values with units and conditions are extractable; adjectives are not.
Entity clarity
How unambiguously a page identifies the organisation, product or standard it describes, and how consistently that identity matches other sources.
Grounding
Tying a generated statement to a retrieved source. Grounded answers cite; ungrounded answers assert from model memory.
Retrieval-augmented generation (RAG)
The pattern where an engine retrieves documents first and writes the answer from them. It is the mechanism that makes citations possible.
AI Overviews
Google's generated summary shown above traditional results, drawing on indexed pages and reducing clicks to the sources beneath it.
Structured data (JSON-LD)
Machine-readable statements of fact embedded in a page — Organization, Product, TechArticle, FAQPage, Dataset, BreadcrumbList — that remove ambiguity for both search and answer engines.
Corroboration
Independent sources repeating a claim. It raises a model's confidence and is the strongest durable driver of being named.
Source authority
The standing of the documents an engine cited to justify an answer. Knowing them tells you which publications to earn a presence on.
Competitor segmentation
Classifying discovered competitors by company type — producer, compounder, additive supplier, OEM, distributor, trader, EPC contractor — so gap analysis compares like with like.
GEO score
EGNITE's 0–100 page score across structured data, entity clarity, citation signals, content authority and crawlability, with evidence behind every deduction.
Fix queue
The ordered list of page-level corrections produced by a scan, sorted by expected impact per unit of effort.
Expert verification
Human review of an AI-generated rewrite by an EGNITE specialist, returned corrected within 48 hours so technical and regulatory accuracy is preserved.
Post-Quantum Cryptography (PQC)
Encryption designed to withstand quantum attack, standardised by NIST as FIPS 203, 204 and 205. Relevant to industrial firms whose encrypted data is being harvested now to decrypt later.

Frequently asked questions

Is GEO the same as AEO or LLMO?

Broadly yes. Answer Engine Optimization (AEO) and LLM Optimization (LLMO) describe the same discipline; GEO is the term used most consistently in research and industry writing.

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Last updated 8 September 2026 · Written by the EGNITE GEO team.