AI glossary for business
The terms you will hear when people talk about AI, explained without jargon and with examples from real companies.
Agentic AI
Agentic AI is an approach in which AI systems receive a goal and pursue it with some autonomy: they plan the steps, use tools, check the results and adjust the plan, instead of answering a single request and stopping.
AI agent
An AI agent is a program that uses a language model to understand a goal, decide which steps to take and carry them out with external tools, such as a calendar, a CRM or email, until it finishes the task or hands it to a person.
GEO (Generative Engine Optimization)
GEO (Generative Engine Optimization) is the set of techniques that help AI answer engines, such as ChatGPT, Perplexity, Gemini or Google's AI overviews, find a website's content, understand it and cite or recommend it in their answers.
LLM (large language model)
A large language model (LLM) is an AI model trained on enormous amounts of text to predict the next word, which lets it understand questions and write, summarize, translate, classify and reason about text.
llms.txt
llms.txt is a Markdown file placed at the root of a website (/llms.txt) that gives language models a summary of the site and a list of links to its most important content. Jeremy Howard proposed it in 2024.
MCP (Model Context Protocol)
MCP (Model Context Protocol) is an open standard, introduced by Anthropic in November 2024, that defines how an AI assistant or agent connects to external tools and data sources, such as a CRM, a database or Google Drive, through one common interface.
n8n
n8n is a workflow automation tool with a visual editor that connects apps, APIs and AI models. It uses a fair-code license, runs in the cloud or on your own server, and Jan Oberhauser created it in Berlin in 2019.
RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) is a technique where an AI system first searches your documents or databases for relevant information, then generates its answer using only that content instead of relying on what the model memorized.
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