AI Glossary
Key concepts in enterprise AI, clearly explained.
An AI agent is an AI system that can take sequences of actions autonomously to accomplish a goal — not just respond to a single prompt, but plan, use tools, check results, and iterate until the task is complete.
Data sovereignty is the principle that data is subject to the laws and governance structures of the country in which it is collected or processed. For enterprises, it determines where data can be stored, who can access it, and under what legal framework.
Fine-tuning is the process of further training a pre-trained AI model on a specific dataset to adapt its behavior for a particular task or domain. It allows organizations to specialize a general-purpose LLM for their industry, writing style, or internal vocabulary.
A large language model is an AI system trained on massive text datasets that can understand and generate human language. Examples include GPT-4, Claude, Llama, and Mistral.
MCP is an open standard developed by Anthropic that allows AI models to connect securely to external tools, data sources, and services. It gives LLMs a standardized way to read files, query databases, call APIs, and take actions — without custom integration code for each tool.
On-premise refers to software and infrastructure that is installed and runs on servers physically located within an organization's own facilities — as opposed to cloud-based solutions hosted by a third-party provider. For AI deployments, on-premise means the model runs on hardware you own and control.
Prompt engineering is the practice of designing and optimizing the instructions given to an AI model to get reliable, high-quality outputs. It is the craft of communicating effectively with LLMs — structuring inputs so the model produces the response you need.
RAG is an AI architecture that combines a language model with a retrieval system, allowing the AI to search your documents and data in real time before generating a response.
A vector database stores data as mathematical vectors (numerical representations of meaning) and enables similarity search — finding content that is semantically similar to a query, not just keyword-matching. It is the storage layer that powers RAG systems.