Glossary
Glossary for AI and digital product development
Short, precise explanations of the terms we use most often — from AI integration and RAG to EHF and GEO. Adapted to Norwegian business context.
- AI agent
- An AI agent is software that sets goals, plans steps, and acts autonomously to achieve them — without continuous human oversight.
- AI integration
- AI integration is the process of wiring language models, RAG, or predictive models directly into a business's existing systems and workflows.
- EHF
- EHF is the Norwegian standard for electronic invoices — mandatory for any supplier to the Norwegian public sector.
- Embedding
- An embedding is a numerical representation of text (or image) that lets AI systems compare semantic similarity.
- Fine-tuning
- Fine-tuning is the process of further training an existing AI model on specific data to improve performance for a narrow use case.
- GEO / AEO
- GEO and AEO are disciplines for getting cited or referenced by AI search engines like ChatGPT, Perplexity, and Google AI Overviews.
- LLM (Large Language Model)
- An LLM is a large language model trained on enormous text volumes that can generate, summarise, and analyse text in a human-like way.
- MCP (Model Context Protocol)
- MCP is an open standard for how AI models connect to external data and tools in a secure, structured way.
- Prompt engineering
- Prompt engineering is the craft of formulating instructions to a language model so it returns consistent, precise, and useful answers.
- RAG (Retrieval-Augmented Generation)
- RAG is a technique where a language model answers based on the business's own documents — instead of only its general training.
- RPA (Robotic Process Automation)
- RPA is software that mimics human clicking in a user interface — typically used to integrate against legacy systems without an API.
- Vector database
- A vector database is a database optimised for storing and searching embeddings — the foundation of RAG systems and AI search.
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