Glossary
The words behind AI products, in plain English.
20 terms on AI, product, search and engineering, each answered in a line first and explained after.
Search · 3 Engineering · 3 AI · 9 Product · 5
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A–Z
Every term.
A
- AEOSearchAnswer engine optimisation (AEO) is the practice of structuring a website so AI assistants and answer engines can find, understand and quote it. It favours direct answers, clear page structure, structured data and content that matches real buyer questions.
- Agentic UXProductAgentic UX is the design of products where AI agents carry out tasks for people. It focuses on how users set goals, see what the agent is doing, approve key actions and correct or undo its work.
- AI agentAIAn AI agent is software that uses a language model to decide which steps to take towards a goal, calling tools such as search, databases or other apps along the way, rather than producing a single reply.
- AI evaluationAIAI evaluation, often called evals, is the structured testing of an AI system against a set of realistic examples with agreed success criteria, so teams can measure quality, catch regressions and decide whether it is ready to release.
- AI hallucinationAIAn AI hallucination is output from a language model that sounds confident but is false, made up or not supported by its sources, such as an invented citation, figure or policy.
H
- Headless CMSEngineeringA headless CMS is a content management system that stores and edits content separately from the website or app that displays it. Content is delivered through an API, so the same content can power many front ends.
- Human-in-the-loopAIHuman-in-the-loop (HITL) is a design approach where people review, approve or correct an AI system's output at defined points, so important decisions are checked by someone accountable before they take effect.
M
- MCPEngineeringThe Model Context Protocol (MCP) is an open standard for connecting AI assistants and agents to external tools and data, such as files, databases and business apps, through a common interface instead of one-off integrations.
- MVPProductA minimum viable product (MVP) is the smallest version of a product that real users can use to show whether it solves their problem. It focuses on one core job, done well, to learn before investing further.
P
- Product discoveryProductProduct discovery is the work of understanding users, their problems and the business constraints before building, so a team decides what to build, and what not to, based on evidence rather than assumptions.
- Prompt engineeringAIPrompt engineering is the practice of writing, structuring and testing the instructions, context and examples given to a language model so it produces useful, consistent output for a specific task.
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