AI Terms Simply Explained.
All important terms from the world of Artificial Intelligence — explained simply for entrepreneurs, managers and teams.
AI Agent
An AI agent is an autonomous software system that independently executes tasks, makes decisions and interacts with its environment — without requiring human intervention at every step.
API
An API is an interface that enables different software applications to communicate with each other. In the AI context, APIs enable the integration of AI services like OpenAI or Anthropic into existing systems.
Automation
Automation refers to the execution of tasks by machines or software without human intervention. AI-powered automation goes beyond rule-based automation and can also handle unstructured data and variable situations.
AI Bias
AI bias refers to systematic distortions in AI models that lead to unfair or incorrect results. Bias often arises from unbalanced training data.
Chatbot
A chatbot is a program that automatically responds to text inputs. Modern AI chatbots are based on large language models (LLMs) and can conduct natural conversations, answer questions and execute tasks.
Computer Vision
Computer Vision is an AI field that enables machines to 'see' — to interpret images and videos. Applications include quality control in manufacturing, facial recognition, medical image analysis and autonomous driving.
Deep Learning
Deep Learning is a subset of machine learning based on artificial neural networks with many layers. It enables machines to recognise complex patterns in large amounts of data — the foundation of modern AI systems like language models and image recognition.
GDPR (General Data Protection Regulation)
The GDPR is the European data protection regulation in force since 2018. Relevant in the AI context for: processing personal data, transparency obligations for automated decisions, data storage and data subject rights.
Embeddings
Embeddings are numerical representations of text, images or other data in a high-dimensional vector space. They enable AI systems to recognise semantic similarities — the foundation for search systems, recommendation algorithms and RAG systems.
EU AI Act
The EU AI Act is the first comprehensive AI regulation of the European Union (in force since 2024). It classifies AI systems by risk categories and defines obligations for providers and users.
Fine-Tuning
Fine-tuning refers to adapting a pre-trained AI model to specific tasks or domains through further training on domain-specific data.
Generative AI
Generative AI refers to AI systems that can create new content — texts, images, audio, video or code. ChatGPT, DALL-E and Claude are well-known examples.
Hallucination
Hallucination refers to the phenomenon where an AI language model generates plausible-sounding but factually incorrect information.
Human-in-the-Loop
Human-in-the-Loop (HITL) describes AI systems where humans are involved at critical decision points. Particularly important in high-risk applications such as medical diagnoses, credit decisions or legal assessments.
Inference
Inference refers to the process of applying a trained AI model to new data to generate predictions or outputs.
AI Readiness
AI readiness describes the maturity level of a company for successful AI deployment. Assessment criteria include: data quality and availability, IT infrastructure, process maturity, employee competencies and organisational structures.
Knowledge Graph
A knowledge graph is a structured representation of knowledge as a network of entities and their relationships.
Large Language Model (LLM)
A Large Language Model (LLM) is an AI model trained on vast amounts of text that can understand and generate natural language. Well-known LLMs include GPT-4 (OpenAI), Claude (Anthropic), Gemini (Google) and Llama (Meta).
Machine Learning (ML)
Machine learning is a subset of AI where systems learn from data without being explicitly programmed. ML algorithms recognise patterns in data and improve their performance through experience.
Multi-Agent System
A multi-agent system consists of multiple AI agents working together in a coordinated manner to solve complex tasks. Each agent specialises in a partial aspect.
Neural Network
An artificial neural network is a computational model inspired by the human brain. It consists of layers of connected nodes (neurons) that process signals.
On-Premise AI
On-premise AI refers to AI systems operated on the company's own IT infrastructure — as opposed to cloud-based services. Particularly relevant for companies with sensitive data and strict data protection requirements.
Orchestration
AI orchestration refers to the coordination and control of multiple AI agents, models or services within an overarching system.
Predictive Analytics
Predictive analytics uses historical data and machine learning models to predict future events. Applications: demand forecasting in retail, predictive maintenance in manufacturing, churn prediction in customer service.
Prompt
A prompt is the input or instruction given to an AI model to generate a desired output. The quality of the prompt directly influences the quality of the AI output.
Prompt Engineering
Prompt engineering is the systematic development and optimisation of input instructions for AI models to achieve consistent and high-quality outputs.
RAG (Retrieval-Augmented Generation)
RAG is a technique that connects language models with an external knowledge base. The model first searches for relevant information from the database and then generates a response based on these specific informations.
Reinforcement Learning
Reinforcement Learning (RL) is an ML method where an agent learns to select actions that maximise a reward through trial and error.
Semantic Search
Semantic search understands the meaning of a search query — not just the keywords. AI-based semantic search delivers more relevant results as it understands the context and intention of the query.
Supervised Learning
Supervised learning is an ML method where a model is trained on labelled data. The model learns to map inputs to known outputs.
Token
In AI processing, tokens are the smallest units into which text is divided. A token corresponds approximately to a word or word part.
Transfer Learning
Transfer learning refers to transferring knowledge from a pre-trained model to a new task.
Vector Database
A vector database stores and searches data as numerical vectors (embeddings). It enables extremely fast similarity searches and is the technical foundation for RAG systems and semantic search functions.
Workflow Automation
Workflow automation refers to the automatic execution of business processes without manual interventions. AI-powered workflow automation goes beyond rule-based systems and can respond to unstructured data, exceptions and variable situations.
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