The most important terms around artificial intelligence, explained in plain language. Six central terms in detail, plus a compact list of the rest.
Anyone who wants to work with AI can't get around a handful of English terms. They look unwieldy - behind each one sits a simple idea.
This glossary explains the six terms you'll hear most often, in two paragraphs each. Plus an appendix with further terms that matter less often but crop up occasionally. As of: 2026.
A large language model is the technology behind ChatGPT, Claude, Gemini and similar services. At its core it's a program that predicts which word would plausibly come next - based on the vast amounts of text it was trained on. When you type "The capital of Germany is..." it knows: Berlin. Not because it knows Berlin, but because Berlin followed this opening millions of times in its training.
That isn't real intelligence in the philosophical sense - but it's an astonishingly useful imitation. From this simple basic idea comes everything you experience as AI today: writing texts, answering questions, generating code, translating language.
A normal LLM only knows what was known at training time. It knows nothing about your company, your customers, your contracts. RAG is the technique that gives an LLM access to your own documents - so that it draws on them when answering.
In concrete terms: you put all your documents into a searchable collection. With every question the system first pulls out the relevant documents and hands them to the LLM to answer. The result: answers based on your real data - not on the model's general knowledge. Almost every sensible AI application in mid-sized companies uses some form of RAG today.
An AI agent is an LLM that doesn't just answer but acts. It can operate programs, send emails, look things up in databases, create orders - in steps it plans itself.
Example: you say "Reply to all enquiries from the last week that are still unanswered with a standard response." An agent checks which enquiries are open, drafts the replies, sends them. That's the next generation of AI application - but it's also the more dangerous one. Agents need clear limits on what they're allowed to do, and ideally a four-eyes principle for anything with consequences.
When an LLM gives an answer that sounds plausible but is factually wrong - that's called a hallucination. It isn't a bug in the narrow sense, but a consequence of how it works: the model predicts what probably fits - not what's true.
Hallucinations are the biggest risk when using AI. They're especially dangerous because they sound convincing. A made-up phone number, a wrong legal clause, a source citation that doesn't exist. Protection: always verify important facts, treat AI answers as a suggestion rather than the truth, use RAG so the model works from real documents.
An embedding is a mathematical translation of text into numbers. More precisely: each text becomes a list of a few hundred or thousand numbers that describe its meaning. Texts with similar meaning have similar number patterns - even when they use completely different words.
A vector database is a store that can handle these embeddings. You file all your documents as embeddings. With a question, the question itself also becomes an embedding - and the database finds the documents closest in meaning. That's the technical basis of RAG (see above).
Every LLM was trained at a particular point in time. What happened after this cutoff it doesn't know - unless it gets current information via RAG or an internet connection. That's why ChatGPT sometimes gives outdated answers to questions about today's weather or the current chancellor.
Training data are the texts the model learned from. With the large models these are billions of web pages, books, scientific articles and code examples. What didn't appear in training, the model doesn't know either. What appeared especially often, it handles especially well.
This glossary grows with what customers ask us. If an important term is missing, write to us - we'll add it.
AI vocabulary isn't static. What was mainstream in 2024 may be outdated by 2026 - and new terms keep arriving. We try to keep this list current without overloading it with buzzwords.
What AI fundamentally is, is under What is AI, really?. What AI can do today is under What AI makes possible. What it can't do is under What AI can't do, even when it looks like it can.