{"id":7825,"date":"2024-05-14T11:18:00","date_gmt":"2024-05-14T09:18:00","guid":{"rendered":"http:\/\/nextbrain.ai\/?p=7825"},"modified":"2024-05-14T11:22:02","modified_gmt":"2024-05-14T09:22:02","slug":"rag-explained-in-simple-words","status":"publish","type":"post","link":"https:\/\/nextbrain.ai\/de\/blog\/rag-explained-in-simple-words","title":{"rendered":"RAG einfach erkl\u00e4rt"},"content":{"rendered":"
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Retrieval-Augmented Generation (RAG) ist die Methode zur Verfeinerung der Ausgabe eines umfangreichen Sprachmodells, indem auf eine glaubw\u00fcrdige Wissensbasis verwiesen wird, die \u00fcber die Trainingsdatenquellen hinausgeht, bevor eine Antwort generiert wird.<\/p>

In diesem Artikel werden wir erkl\u00e4ren, wie es tats\u00e4chlich in einfachen Worten funktioniert.<\/p>

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Stellen Sie sich vor, wir haben einen Benutzer. Der Benutzer k\u00f6nnte eine Person, ein Bot oder eine andere Anwendung sein, die alle Antworten auf spezifische Fragen sucht. Zum Beispiel k\u00f6nnten sie fragen: \u201eWie hoch war die Churn-Rate im Q1 f\u00fcr Kunden in der S\u00fcdregion?\u201c<\/p>

Der erste Teil dieser Frage liegt typischerweise im Bereich eines allgemeinen Large Language Model (LLM). Allerdings sind die spezifischen Daten, die f\u00fcr \u201eQ1 von Kunden im S\u00fcden\u201c ben\u00f6tigt werden, in LLMs nicht direkt verf\u00fcgbar, da sie einzigartig f\u00fcr das Unternehmen sind und sich im Laufe der Zeit \u00e4ndern.<\/p>

Um dies zu verwalten, k\u00f6nnten mehrere Datenquellen erforderlich sein. Dazu k\u00f6nnten PDFs, andere Gesch\u00e4ftsanwendungen oder sogar Bilder geh\u00f6ren. Der Zugriff auf die richtigen Daten ist entscheidend, um solche spezifischen Anfragen genau zu beantworten.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Die Rolle von Vektor-Datenbanken in RAG<\/strong><\/h3>

Sobald Sie die erforderlichen Daten gesammelt haben, werden diese in eine Vektor-Datenbank<\/span>. Diese Datenbank organisiert sowohl strukturierte als auch unstrukturierte Daten in einem mathematischen Array-Format, das f\u00fcr Machine Learning und generative KI-Modelle im Vergleich zu Rohdaten verst\u00e4ndlicher ist. Durch die Abfrage dieser Vektor-Datenbank erhalten Sie ein Embedding, das Daten enth\u00e4lt, die f\u00fcr Ihre Frage relevant sind.<\/p>

Dieses Embedding wird dann wieder in das LLM eingef\u00fchrt, wodurch der urspr\u00fcngliche Prompt mit pr\u00e4zisen, belegten Daten angereichert wird. Das LLM verarbeitet diesen erweiterten Prompt und liefert die Antwort auf die urspr\u00fcngliche Frage und stellt dabei Genauigkeit und Relevanz sicher.<\/p>

Wenn neue Daten in die Vektordatenbank eingegeben werden, aktualisiert sie die Embeddings, die f\u00fcr laufende Abfragen wie die Abwanderungsrate im Q1 relevant sind. Diese kontinuierliche Aktualisierung stellt sicher, dass nachfolgende Abfragen die aktuellsten und relevantesten Informationen erhalten.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Risiken in der KI-getriebenen Datenanalyse mindern<\/strong><\/h3>\n

Die Qualit\u00e4t der Daten, die in die Vektordatenbank eingegeben werden, ist sehr wichtig f\u00fcr die produzierten Ergebnisse. Saubere, gut verwaltete und ordnungsgem\u00e4\u00df verwaltete Daten zu gew\u00e4hrleisten, ist entscheidend. Dar\u00fcber hinaus ist die Transparenz der im Prozess verwendeten LLMs ebenfalls entscheidend. Es ist wichtig, LLMs zu verwenden, die in ihren Trainingsprozessen transparent sind, um Zuverl\u00e4ssigkeit und Genauigkeit zu gew\u00e4hrleisten.<\/p>\n

Bei NextBrain AI<\/a>, wir nutzen die neueste AI-Technologie, um pr\u00e4zise Datenanalysen und umsetzbare Gesch\u00e4ftsinformationen bereitzustellen, ohne die oft mit technischen Implementierungen verbundenen Komplikationen. Vereinbaren Sie noch heute Ihre Demo<\/a> um firsthand zu erleben, wie unsere L\u00f6sung funktioniert.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\"Demo\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Retrieval-Augmented Generation (RAG) is the method of refining the output of a substantial language model by referring to a credible knowledge base beyond its training […]<\/p>\n","protected":false},"author":1,"featured_media":7826,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[70],"tags":[598,601,290,434,264,597,600,282,587,599],"class_list":["post-7825","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-ai-driven-data-analysis","tag-business-insights","tag-data-analysis-2","tag-data-governance","tag-generative-ai","tag-large-language-model","tag-llm","tag-machine-learning","tag-rag","tag-vector-database"],"yoast_head":"\nRAG Explained in Simple Words - NextBrain AI | No-Code Machine Learning<\/title>\n<meta name=\"description\" content=\"Discover how Retrieval-Augmented Generation (RAG) revolutionizes data handling with vector databases and large language models.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/nextbrain.ai\/de\/blog\/rag-explained-in-simple-words\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"RAG Explained in Simple Words - 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