All GuidesGuide 05
Fortgeschritten • ~50min

RAG — Retrieval-Augmented Generation

Ground AI in your documents with RAG. Learn chunking strategies, vector embeddings, and hallucination reduction techniques.

Machen Sie Ihre KI-Antworten präziser, indem Sie sie in Ihren eigenen Daten verankern.

Ziele

01Das RAG-Prinzip (Retrieval-Augmented Generation) verstehen
02Eine einfache RAG-Pipeline mit Vektordatenbank implementieren
03Chunk-Strategien und Abrufqualität optimieren

Inhaltsverzeichnis

Section 01
Einführung in RAG: Warum externe Daten integrieren?
Section 02
Vektorisierung, Embeddings und semantischer Abruf.
Section 03
Übung: ein Dokumentenset indexieren und mit RAG abfragen.
Section 04
Optimierung: Chunking, Metadaten und Reranking.

Fähigkeiten

Eine RAG-Pipeline mit Vektordatenbank implementieren.Dokumente für optimalen Abruf chunken und indexieren.Abrufqualität mit Metadaten und Reranking verbessern.

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Häufig gestellte Fragen

Was ist das Modul „RAG — Retrieval-Augmented Generation“?+

„RAG — Retrieval-Augmented Generation“ ist ein Online-Schulungsmodul auf Fortgeschritten • ~50min-Niveau. Ground AI in your documents with RAG. Learn chunking strategies, vector embeddings, and hallucination reduction techniques.

Gibt es Voraussetzungen für dieses Modul?+

Ja, wir empfehlen, Modul 4 vorher abzuschließen.

Ist dieses Modul kostenlos?+

Ja, dieses Modul ist vollständig kostenlos und ohne kostenpflichtiges Abonnement zugänglich.

Was werde ich in diesem Modul lernen?+

Eine RAG-Pipeline mit Vektordatenbank implementieren.. Dokumente für optimalen Abruf chunken und indexieren.. Abrufqualität mit Metadaten und Reranking verbessern..