Knowledge base
AI Glossary
Key terms in artificial intelligence – precisely explained, continuously updated and linked to our current coverage.
- AI agent
An AI agent uses a language model to pursue goals autonomously across multiple steps – with planning, tool calls and feedback loops.
- Context window
The context window is the maximum amount of text in tokens a language model can process per call – it bounds knowledge, history and cost.
- Embedding
Embeddings are vector representations of content that map semantic similarity to spatial proximity – the basis of semantic search and RAG.
- EU AI Act
The EU AI Act regulates AI systems on a risk basis – with staggered obligations from banned practices to high-risk requirements from 2026/27.
- Fine-tuning
Fine-tuning adapts a pre-trained language model with task-specific data – suited to style, format and domain language, less to factual knowledge.
- Generative Engine Optimization (GEO)
Generative engine optimization (GEO) prepares content to be cited in answers from AI search engines and chatbots – SEO for the AI era.
- Hallucination
Hallucinations are plausible-sounding but false outputs of language models – the central risk in production use of generative AI.
- Large Language Model (LLM)
Large language models are neural networks that model language statistically and generate text – the foundation of modern generative AI systems.
- Model Context Protocol (MCP)
The Model Context Protocol (MCP) is an open standard that connects AI applications to external tools and data sources in a uniform way.
- Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation enriches language model answers with externally retrieved knowledge – the standard for current and internal data.