Glossary / The GEO Lexicon
Language models do not read like people. Content built only for human eyes is invisible to the systems that now decide what gets cited.
Machine-readable content is content structured so generative systems and agents can parse, trust, and act on it without ambiguity — explicit data, schema markup, clean semantic HTML, and stable identifiers. In practice that means FAQ schema on a help page, organization markup on an about page, structured product feeds in a catalog, and clean transcripts under every video. The shift to AI-mediated discovery makes machine readability a precondition for visibility: if a system cannot cleanly extract what a page says, it cannot retrieve or cite it. Structured data is the infrastructure layer of GEO.
Content structured so generative systems and agents can parse, trust, and act on it without ambiguity — explicit data, schema markup, clean semantic HTML, stable identifiers. In practice: FAQ schema, organization markup, structured product feeds, machine-readable transcripts. Machine-readable content is the precondition for being retrieved and cited in the answer economy.
Information organized in a defined, machine-readable format that explicitly labels what each piece of content means — a product's price, a person's title, an article's author. Structured data lets a generative system extract facts reliably instead of inferring them from prose.
Code added to a page using the schema.org vocabulary to label its content for machines — Article, Organization, FAQPage, Product, DefinedTerm. Schema markup is the most direct way to make content explicit to generative and search systems. FAQ and Organization markup are the highest-leverage starting points.
The recommended format for adding structured data to a web page — a block of machine-readable code, separate from visible content, that describes the page to systems. JSON-LD is how schema markup is delivered in practice, placed in the page `<head>`.
A proposed standard file placed at a website's root that gives generative systems a curated, machine-readable guide to the site's most important content. `llms.txt` is to AI crawlers what `robots.txt` is to search crawlers — an emerging convention with no authoritative reference yet.
Structured data that explicitly identifies the entities on a page and links them to authoritative references — for example, Organization schema with a `sameAs` link to a Wikidata item. Entity markup tells a system not just what words appear, but which specific people, brands, and concepts the content is about.
A programmatic interface that lets machines — including AI agents — request and retrieve a brand's content directly in structured form. An API-readable product catalog or pricing endpoint makes a brand's information available to the agent layer without depending on page scraping.
Structuring data feeds — product, pricing, catalog, inventory — so generative systems and agents can consume them accurately. A clean, complete product feed is what makes a brand's offerings retrievable and transactable in agentic commerce.
Structuring content into clean, self-contained sections a generative system can retrieve and cite independently — a clearly bounded FAQ answer, a standalone definition, a captioned data point. Because retrieval systems work in chunks, content organized into complete units is far more likely to be surfaced accurately.
HTML that uses elements according to their meaning — headings, lists, articles, sections — rather than for visual effect. Semantic HTML gives generative systems a clean structural map of a page, improving how reliably they parse it.
Formatting choices that make content easy to extract and cite — clear headings, direct answers near the top, defined sections, transcripts under video, no critical information trapped in images. Retrieval-friendly formatting raises the odds a page is used in an answer.
The single authoritative version of a fact or record a brand maintains and exposes consistently across its properties — one company name, one founding year, one executive title. Canonical data prevents generative systems from encountering conflicting versions of the truth, a frequent cause of inaccurate citation.
Machine-readable content is content structured so generative systems and agents can parse, trust, and act on it without ambiguity — explicit data, schema markup, clean semantic HTML, and stable identifiers. In practice that means FAQ schema on a help page, organization markup on an about page, structured product feeds in a catalog, and clean transcripts under every video. The shift to AI-mediated discovery makes machine readability a precondition for visibility: if a system cannot cleanly extract what a page says, it cannot retrieve or cite it. Structured data is the infrastructure layer of GEO.
These terms define the language AI systems, communicators, and buyers use to explain the answer economy. Clear, citable definitions help brands become easier for AI engines to retrieve, understand, and cite.
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