AI Glossary: The most important AI terms, simply explained

Agentic AI describes systems that not only respond to a single input, but also actively pursue a multi-stage workflow. They plan intermediate steps, obtain information, use tools and evaluate results.

The term helps to understand the difference between a simple chatbot and a productive AI agent with real process logic.

Artificial intelligence describes systems that automate human-like tasks such as understanding, decision-making and communicating. Today, AI primarily includes machine learning and generative models.

AI is the underlying technology behind all modern automation – from chatbots to autonomous agents.

An AI agent is a system that can independently complete tasks in multiple steps. It plans, uses tools, makes decisions, and self-corrects when needed.

Agents are the next step in evolution: from individual AI responses to complete, automated workflows.

AI governance includes policies, processes and controls that ensure that AI is used responsibly, transparently and legally.

With the EU AI Act and increasing AI usage, governance is becoming mandatory. Companies need clear rules before they use AI productively.

Clawdbot and Moltbot often appear in the market and in conversations as historical or colloquial names in the OpenClaw environment. What is particularly important for companies is to clearly relate the terms to today's framework and its context of use.

This family of terms is important for topical authority, internal linking and picking up fuzzy search queries in the market.

Chunking breaks long texts into smaller pieces (chunks) so that they can be saved as embeddings and found effectively. The chunk size affects the quality of the search.

Good chunking determines the quality of RAG systems. Chunks that are too large dilute the search, and chunks that are too small lose the context.

Embeddings convert text into number vectors that capture semantic meaning. Similar concepts are close to each other in vector space, regardless of the exact formulation.

Embeddings enable semantic search - the system finds relevant information even if other words are used.

Evals are tests and metrics used to measure the quality of AI outputs. They help to identify whether a system works reliably and where weaknesses lie.

Without Evals you fly blind. Systematic evaluation is the basis for continuous improvement and trust in AI systems.

Generative AI refers to systems that can generate new content instead of just analyzing existing data. These include models such as ChatGPT, DALL-E or Claude.

GenAI has made AI accessible to millions of users and enables entirely new types of automation.

Grounding is the technique of linking AI answers to verifiable facts or sources. Instead of generating freely, the model only responds based on specific documents or data.

Grounding drastically reduces hallucinations and makes AI answers reliable and verifiable.

A hallucination occurs when an AI model invents facts that sound plausible but are not true. This happens because the model is based on probabilities - not truth.

Hallucinations are the biggest trust risk when using AI. Countermeasures such as grounding and RAG are therefore essential.

Human-in-the-loop means that a human is involved in important or uncertain decisions. The AI works autonomously, but human approval is obtained at defined points.

HITL is the key to trustworthy AI in business use – automation with a safety net.

LLMs are neural networks with billions of parameters trained on massive amounts of text. They can understand, generate, translate language, and perform tasks such as summarizing or creating code.

LLMs are the engine behind most current enterprise AI applications – from customer service to document processing.

Machine learning is a branch of AI in which algorithms learn independently from data. Instead of manually defining rules, the system recognizes patterns and improves over time.

ML is the foundation for almost every modern AI application – from speech recognition to fraud detection.

OpenClaw is a framework for AI agents that can process tasks across multiple steps using context, tools and rules. It is particularly suitable for companies that want to combine data sovereignty, integrations and controlled automation.

OpenClaw is interesting for companies because it positions itself between classic workflow automation and completely closed AI platforms.

On-premise hosting means that central components of an AI system are operated within your own infrastructure or in a controlled data center environment. This is particularly relevant for sensitive data and strict compliance requirements.

On-premise is a key decision criterion for companies that need data sovereignty, low dependency and regulatory control.

A prompt is the text you send to an AI model - a question, instruction, or context. The quality of the prompt largely determines the quality of the answer.

Prompt design is a core skill when dealing with AI - the right instruction makes the difference between useless and brilliant output.

Privacy sets clear requirements for the handling of personal data in AI systems. Companies must ensure that AI applications work in compliance with data protection regulations.

Privacy violations can be expensive (up to 4% of annual sales). Data protection must be considered from the start - not as an afterthought.

RAG combines information search with text generation. Instead of just answering from the training, the system first searches a knowledge base and uses the documents found as a basis.

RAG is the standard approach to equip LLMs with company-specific knowledge - without expensive follow-up training.

During training, a model learns from data and builds its knowledge. In inference, it applies this knowledge to answer queries. Training happens once (or rarely), inference runs constantly.

Helps to understand why AI models can be expensive to develop but inexpensive to operate.

Tokens are the smallest units of text that an LLM processes – approximately ¾ of a word. The context window defines how many tokens the model can "see" at the same time.

The context window determines how much information a model can take into account at the same time - and influences costs and quality.

The temperature controls how "random" the text generation is. Low temperature (0) = deterministic and focused. High temperature (1+) = more creative and variable.

The right temperature setting is crucial - too high leads to nonsense, too low to rigid repetitions.

Function calling enables LLMs to not only generate text, but to trigger structured actions - e.g. query a database, send an email or perform a calculation.

Function calling turns LLMs from pure text generators into actionable systems that can intervene in real business processes.

A vector database stores embeddings and enables extremely fast similarity searches. It is the central storage system for RAG applications and semantic search.

RAG would not be possible without a vector database. They are the infrastructure behind every AI that works with its own data.