Core concepts for language models, retrieval systems, and machine-readable information.
How likely an AI shopping assistant is to find, consider, and recommend a product — based on how well that product's information is structured and how trustworthy it appears.
Signals that tell AI systems a source can be trusted — like being cited by reputable sources, having consistent expert content, and maintaining a reliable track record.
A map of which sources cite which other sources — helping AI systems understand which content is considered authoritative by the broader body of knowledge.
A score indicating how credible and influential a website is in its topic area — higher-authority sites are more likely to be cited in AI-generated responses.
How clearly and consistently a thing is described in data — making it easy for AI systems to find and correctly identify it without confusing it with something else.
Figuring out that 'Apple Inc.', 'AAPL', and 'Apple Computer Company' all refer to the same organisation — so data about that entity can be unified.
A machine-readable map of facts about the world — connecting entities like companies, people, and products to each other and their attributes in a structured, queryable way.
A very large AI model trained on huge amounts of text that can write, answer questions, summarise, and converse in natural language.
Information in a format that computers can understand directly without needing a human to read and interpret it first.
Testing and measuring how well a RAG system works — checking whether it finds the right information, uses it accurately, and attributes it correctly in its answers.
How good a search or AI retrieval system is at finding the right information — returning what is genuinely relevant without missing important results or including irrelevant ones.
The component of an AI system that finds relevant information in a knowledge base or document collection in response to a question or query.
An AI approach that looks up relevant information in real time before generating an answer — combining the flexibility of language models with the accuracy of up-to-date knowledge retrieval.
A shared vocabulary for labelling web content so that search engines and AI systems understand what a page is about — whether it is a product, an article, a business, or a person.
Showing your work in AI — identifying which sources an AI response drew on, so users can verify claims and understand where the information came from.
Data in a defined, organised format that computers can read and understand directly — like a spreadsheet or a labelled JSON file — as opposed to free text.
A formal structure that defines what makes something trustworthy in a digital system and how that trustworthiness is established and communicated to users.
A concrete tool or method that helps establish or check whether something in a digital system can be trusted — from confidence scores to source verification badges.