Technique Aktualisiert 2026-04

RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation
Definition

RAG is a technique that connects an LLM to external data sources to generate more accurate and up-to-date answers.

Siehe auch im Glossar

L
LLM (Large Language Model)
An LLM is an AI model trained on billions of texts, capable of understanding and generating human language.
P
Prompt
A prompt is the instruction or question you give an AI to get a response. It's the interface between you and the model.
A
AI Agent
An AI agent is an autonomous system that uses an LLM to plan, decide and execute real tasks without human intervention at each step.
G
Generative AI
Generative AI refers to artificial intelligence systems capable of creating original content: text, images, video, audio, code.

Tools, die rag verwenden

P
Perplexity
Die KI-Antwortmaschine, die ihre Quellen zitiert
4.5/5
P
Pinecone
Die führende Vektor-Datenbank für KI-Anwendungen
4.6/5
C
Consensus
Die KI-Suchmaschine für wissenschaftliche Literatur
4.7/5
N
NotebookLM
Googles KI, die Ihre Dokumente in einen Experten-Assistenten verwandelt
4.5/5

Häufig gestellte Fragen

What's the difference between RAG and fine-tuning?
Fine-tuning modifies the model itself by retraining it on your data. RAG leaves the model intact and feeds it relevant information at query time. RAG is simpler, cheaper and keeps data up-to-date.
Which tools use RAG?
Perplexity (web search + AI), NotebookLM (document analysis), and most enterprise chatbots connected to an internal knowledge base.