Glossary of Terms

AEO (Answer Engine Optimisation) is the discipline of structuring content so AI-powered search systems can extract and cite it as a direct answer. Unlike traditional SEO, which seeks ranking position, AEO seeks citation; direct attribution of content as a source in an AI-generated answer. It requires correct entity type declaration, direct Answer First structure, schema completeness, and machine-readable structured data.

GEO (Generative Engine Optimisation) is the practice of structuring content to be cited by AI systems that synthesise answers without the user visiting the original page. GEO asks what must be true about content for a machine to cite it when composing a response from multiple sources. The criteria centre on entity clarity, schema completeness, source trustworthiness, and content that survives the compression an AI system applies when extracting and restating an answer.

The citation economy describes the shift from a ranking economy, where position on a results page determines exposure, to an attribution economy, where being cited as a source in an AI-generated answer provides visibility independent of ranking position. In the citation economy, a page ranking in position 21 can be cited ahead of one ranking in position 1 if its entity declarations and structured content are clearer.

The three-objective quality standard is the framework's definition of good content: content that has met its human objective, its machine objective, and its philosophical objective within reason. The human reader must find it clear, useful, and trustworthy. The machine reader must find it structured, entity-defined, and schema-valid. The philosophical test must confirm that editorial intent is explicit, quality is repeatable, and expertise is genuine. No one audience may be sacrificed for the convenience of the other two.

Schema markup is machine-readable structured data embedded in a web page that tells AI systems and search engines what the content is, who it belongs to, and how it relates to other entities. Within The Content Framework, schema is treated as infrastructure: the canonical machine-readable expression of editorial truth. Schema represents what is true about content; it does not create truth. Thin content with complete schema remains thin content.

The unified @graph is a single JSON-LD block containing a connected set of entity declarations that explicitly reference each other. Rather than emitting separate disconnected schema declarations, every CQIP page emits one @graph array where the Service entity references the Organisation, the Organisation references the Person, and the Person references their credentials. Every relationship is declared, not inferred.

Entity recognition is the state in which an AI system has associated a page's schema declarations with the relevant query set, independent of ranking position. A page can have entity recognition without significant organic traffic, and can rank highly without entity recognition. Entity recognition is established when an AI system has encountered a page's entity declarations and associated them with relevant query categories. It is the upstream condition that determines whether a page can be cited.

The pre-publish quality gate is the validation mechanism that prevents publication of content that has not met the framework's minimum structural requirements. It returns a quality score from A+ to F. Errors are blocking failures; the page cannot be published until they are resolved. Warnings are advisory. The gate is not a recommendation system; it is a publishing constraint. Quality is a property of the system rather than a product of individual talent.

Schema drift is the gradual divergence between a page's schema declarations and the current truth they represent, caused by real-world changes not reflected in the structured data. Common examples include expired compliance credentials still declared as current, products declared as in stock after depletion, and personnel schema referencing individuals who have left the organisation. Schema drift transforms valid schema into misrepresentation and is detected by post-publish schema validation.

The GEO test is the framework's most direct measure of whether machine comprehension work has succeeded. It involves posing a question to a generative AI system that CQIP-structured content definitively answers, then observing whether the content appears as a cited source. If correctly structured, entity-defined, and schema-valid content does not appear as a cited source within a few weeks of publication and indexing, it indicates a structural gap. No promotional activity resolves a structural gap; only editorial correction does.

An editorial contract is a governance instrument that defines what editors may and may not do for a specific content type. It specifies mandatory components, quality thresholds, a pre-publish gate, and post-publish review triggers. The four core contracts are the EERC for Entity Review, the AEC for Articles, the SEC for Service pages, and the PEC for Products. The contracts place editorial authority above the schema layer; the editor defines truth, and the schema layer represents it.

The Answer First block is the opening block on every CQIP page. It must begin with a direct declarative statement, not a preamble or description, and generates the page's primary schema entity description. A minimum word count applies; below threshold the block cannot anchor a reliable entity declaration. It is the most consequential block on any page because it is what AI systems read first when determining whether the content is relevant to a given query.

The FAQ block generates FAQPage schema and is the most frequently cited block type in AI-generated responses. It supports both visible questions, displayed to readers, and schema-only questions, declared in structured data only to extend indexed query coverage without increasing page length. Each answer must begin with a direct response to the question in the first sentence. The split between visible and schema-only questions is one of the framework's most significant practical contributions to structured content practice.

The Compliance block declares certifications, accreditations, and regulatory references with validity dates and issuing bodies. It generates EducationalOccupationalCredential schema for each entry. Expired credentials are treated as errors, not warnings, because they misrepresent the provider's current status in machine-readable form. The Compliance block is consistently the most underused block in standard implementations; organisations list credentials in prose without ever declaring them in schema where they function as machine-readable trust signals.

Decorative schema is markup that is technically valid in format but semantically hollow; sufficient to pass a technical audit but insufficient to establish machine comprehension or citation eligibility. It may declare an Organisation type with a name and URL but provide no relationships to services, people, credentials, or geographic area. An AI system reading a page with decorative schema sees that an organisation exists; it cannot establish what the organisation does, for whom, to what standard, or why it should be trusted.

Coverage Intelligence is a content audit methodology that identifies knowledge gaps rather than traffic gaps. A knowledge gap is a subject on which an organisation has genuine expertise but has not yet expressed it as structured, machine-readable content. Coverage Intelligence maps what the organisation knows against what it has declared, identifies the gap, and produces a prioritised list of knowledge areas where structured content would compound authority rather than replicate existing coverage.

The migration sequence is the three-phase approach to bringing existing content into framework compliance. Phase 1 is reclassification: correct page type declarations, URL slugs, and schema types without rewriting content. Phase 2 is structural completion: add missing blocks at minimum specification. Phase 3 is editorial deepening: expand substance once structure is sound. The sequence is deliberate; editorial deepening without structural foundation produces high-quality content that machines cannot fully process.

Trust Declaration is the author credentialling component that generates Person schema with verifiable credentials, professional affiliations, and identifiers. In the citation economy, uncredentialled expertise is structurally equivalent to anonymous expertise. A source whose author cannot be verified in structured data is citation-weaker than one whose author has declared verifiable credentials, regardless of prose quality.

Information Gain is the presence of specific, verifiable claims in content that are not available in the opening paragraph of competing content on the same topic. Under the Article Editorial Contract, a minimum of three Information Gain markers are required. These are declared in schema as ItemList with ListItem elements, making the claims machine-readable as well as human-readable. Content that cannot demonstrate Information Gain is structurally indistinguishable from a restatement of what already exists.

The minimum viable implementation is the six-step threshold at which CQIP begins producing content that measurably surpasses conventional practice. The steps are: adopt the five principles as editorial policy; implement Answer First and FAQ blocks to minimum specification; correct page types and URL slugs for new content; implement author credentialling via Trust Declaration; run AEOS diagnostic validation before every publication; begin Coverage Intelligence auditing. Each step creates the foundation the next requires.