Wiki-Lexikon
8 terms from AI, LLMs, agents and automation — clearly explained.
What is an AI agent?
An AI agent is software that uses a language model to plan and act toward a goal — calling tools, making decisions and running multi-step tasks autonomously. Definition, how it works and examples.
ReadAI agent vs. chatbot — what’s the difference?
A chatbot answers messages; an AI agent pursues a goal — planning, using tools and taking action over multiple steps. The clear difference, with examples and when to use each.
ReadWhat is AI automation?
AI automation uses language models and AI agents to handle tasks that need understanding and judgment — not just fixed rules. Definition, how it differs from classic automation, and real examples.
ReadWhat is an LLM (large language model)?
A large language model (LLM) is an AI trained on huge amounts of text to predict and generate language. Definition, how it works, tokens, context window and where the limits are.
ReadWhat is MCP (Model Context Protocol)?
MCP (Model Context Protocol) is an open standard that lets AI models connect to tools and data sources through one consistent interface. Definition, how it works and why it matters for AI agents.
ReadWhat is prompt engineering?
Prompt engineering is the craft of writing instructions that get reliable, accurate results from an LLM. Definition, core techniques (context, examples, structure) and why it matters in production.
ReadWhat is RAG (retrieval-augmented generation)?
RAG (retrieval-augmented generation) feeds an LLM relevant, current data at query time so its answers are grounded in your facts — not just its training. Definition, how it works and why it matters.
ReadWhat is a vector database (and embeddings)?
A vector database stores text as embeddings — numeric vectors of meaning — so AI can search by similarity, not just keywords. Definition, how embeddings work and why vector search powers RAG.
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