{"id":8192,"date":"2024-07-19T10:14:11","date_gmt":"2024-07-19T08:14:11","guid":{"rendered":"http:\/\/nextbrain.ai\/?p=8192"},"modified":"2024-07-19T10:14:14","modified_gmt":"2024-07-19T08:14:14","slug":"fine-tuning-or-rag-whats-the-best-approach","status":"publish","type":"post","link":"https:\/\/nextbrain.ai\/de\/blog\/fine-tuning-or-rag-whats-the-best-approach","title":{"rendered":"Fine-Tuning oder RAG: Was ist der beste Ansatz?"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"8192\" class=\"elementor elementor-8192\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6be8833 e-flex e-con-boxed e-con e-parent\" data-id=\"6be8833\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-841bcdd e-con-full e-flex e-con e-child\" data-id=\"841bcdd\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-39efe0d elementor-widget elementor-widget-text-editor\" data-id=\"39efe0d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p class=\"p1\">Angenommen, Sie m\u00fcssen einen AI Kundenservice-Chatbot erstellen. Selbst wenn Ihr Model mit einem spezifischen Trainingsdatensatz feinabgestimmt ist, w\u00e4re es ineffektiv ohne Zugriff auf Daten wie vergangene Gespr\u00e4che oder Produktinformationen, die in den CRMs, Dokumenten oder Ticketsystemen der Kunden gespeichert sind.<\/p><p class=\"p1\">Um diese kontextuellen Daten zu nutzen, m\u00fcssen Sie sie mit Ihren LLMs integrieren. Dies umfasst die Datenaufnahme von Drittquellen und die Wahl zwischen RAG und Feinabstimmung, um die Daten effektiv zu nutzen.<\/p><p>Aber was ist der beste Ansatz \u2013 Fine-Tuning oder Retrieval Augmented Generation (RAG)? Dieser Artikel bietet einen detaillierten Vergleich zwischen ihnen.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6546671 e-con-full e-flex e-con e-child\" data-id=\"6546671\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-91e539a elementor-widget elementor-widget-image\" data-id=\"91e539a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"300\" height=\"300\" src=\"https:\/\/nextbrain.ai\/wp-content\/uploads\/2024\/05\/RAG-or-Fine-tuning-300x300.png\" class=\"attachment-medium size-medium wp-image-8193\" alt=\"\" srcset=\"https:\/\/nextbrain.ai\/wp-content\/uploads\/2024\/05\/RAG-or-Fine-tuning-300x300.png 300w, https:\/\/nextbrain.ai\/wp-content\/uploads\/2024\/05\/RAG-or-Fine-tuning-1024x1024.png 1024w, https:\/\/nextbrain.ai\/wp-content\/uploads\/2024\/05\/RAG-or-Fine-tuning-150x150.png 150w, https:\/\/nextbrain.ai\/wp-content\/uploads\/2024\/05\/RAG-or-Fine-tuning-768x768.png 768w, https:\/\/nextbrain.ai\/wp-content\/uploads\/2024\/05\/RAG-or-Fine-tuning-12x12.png 12w, https:\/\/nextbrain.ai\/wp-content\/uploads\/2024\/05\/RAG-or-Fine-tuning.png 1080w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/>\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-4bdb044 e-flex e-con-boxed e-con e-parent\" data-id=\"4bdb044\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1a1630b elementor-widget elementor-widget-text-editor\" data-id=\"1a1630b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3>Retrieval Augmented Generation (RAG)<\/h3><p>RAG verbessert die Genauigkeit von LLMs, indem es externe Daten bedarfsgerecht abruft und Kontext in die Eingabeaufforderungen zur Laufzeit einf\u00fcgt. Diese Daten k\u00f6nnen aus verschiedenen Quellen stammen, wie z.B. Kundendokumentation, Webseiten und Drittanwendungen wie CRMs und Google Drive.<\/p><h4>Schl\u00fcsselelemente von RAG<\/h4><ol><li><p><strong>Datenaufnahme und Speicherung<\/strong>:<\/p><ul><li><strong>Erste Aufnahme<\/strong>: Ziehen Sie zun\u00e4chst alle relevanten Kundendaten.<\/li><li><strong>Laufende Updates<\/strong>: Verwenden Sie Hintergrundjobs, um die Daten in Echtzeit aktuell zu halten.<\/li><li><strong>Embeddings und Speicherung<\/strong>: Speichern Sie die Daten in einer Vektordatenbank f\u00fcr die Abfrage.<\/li><\/ul><\/li><li><p><strong>Prompt-Injektion<\/strong>:<\/p><ul><li><strong>Zur Laufzeit<\/strong>: Rufen Sie relevante Textabschnitte aus der Vektordatenbank ab und injizieren Sie sie in den urspr\u00fcnglichen Prompt\/Abfrage, damit das LLM die endg\u00fcltige Antwort generiert.<\/li><\/ul><\/li><\/ol><h3>Feinabstimmung<\/h3><p>Die Feinabstimmung umfasst das weitere Training eines vortrainierten LLM auf einem dom\u00e4nenspezifischen Datensatz, um seine Leistung bei bestimmten Aufgaben zu verbessern. Zum Beispiel die Feinabstimmung eines Modells auf Verkaufs-E-Mails, um einen KI-Verkaufsagenten zu entwickeln.<\/p><h4>Herausforderungen beim Fine-Tuning<\/h4><ul><li><strong>Datenvorbereitung<\/strong>: Ben\u00f6tigt ein sauberes, gut strukturiertes Training-Dataset.<\/li><li><strong>Vorhersehbare Ergebnisse<\/strong>: Produziert vorhersehbarere Ergebnisse, ist jedoch zeitaufwendig.<\/li><\/ul>\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<div class=\"elementor-element elementor-element-f0e4e9e e-flex e-con-boxed e-con e-parent\" data-id=\"f0e4e9e\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4264de2 elementor-widget elementor-widget-text-editor\" data-id=\"4264de2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 style=\"text-align: center;\"><strong>RAG vs. Fine-Tuning: Welche Wahl treffen?<\/strong><\/h2>\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<div class=\"elementor-element elementor-element-190eb35 e-flex e-con-boxed e-con e-parent\" data-id=\"190eb35\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-66b3de7 e-con-full e-flex e-con e-child\" data-id=\"66b3de7\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a0ba985 elementor-widget elementor-widget-text-editor\" data-id=\"a0ba985\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3>Wann RAG verwenden<\/h3><ul><li>Injiziert Echtzeitkontext in die Eingabeaufforderungen.<\/li><li>Ben\u00f6tigt kein strukturiertes Training-Dataset.<\/li><li>Ruft relevanten Kontext aus mehreren Datenquellen ab.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-27cc50b e-con-full e-flex e-con e-child\" data-id=\"27cc50b\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ffb75a5 elementor-widget elementor-widget-text-editor\" data-id=\"ffb75a5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3>Wann man Fine-Tuning verwenden sollte<\/h3><ul><li>Wenn Sie einen spezifischen, gut vorbereiteten Datensatz f\u00fcr das Training haben.<\/li><li>F\u00fcr Aufgaben, die vorhersehbare Ergebnisse erfordern.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\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<div class=\"elementor-element elementor-element-6fd8455 e-flex e-con-boxed e-con e-parent\" data-id=\"6fd8455\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0815f9f elementor-widget elementor-widget-text-editor\" data-id=\"0815f9f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2><strong>Implementierung von RAG<\/strong><\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2d09cc6 elementor-widget elementor-widget-image\" data-id=\"2d09cc6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"580\" height=\"264\" data-src=\"https:\/\/nextbrain.ai\/wp-content\/uploads\/2024\/05\/RAgAS.png\" class=\"attachment-large size-large wp-image-7815 lazyload\" alt=\"\" data-srcset=\"https:\/\/nextbrain.ai\/wp-content\/uploads\/2024\/05\/RAgAS.png 770w, https:\/\/nextbrain.ai\/wp-content\/uploads\/2024\/05\/RAgAS-300x136.png 300w, https:\/\/nextbrain.ai\/wp-content\/uploads\/2024\/05\/RAgAS-768x349.png 768w, https:\/\/nextbrain.ai\/wp-content\/uploads\/2024\/05\/RAgAS-18x8.png 18w\" data-sizes=\"(max-width: 580px) 100vw, 580px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 580px; --smush-placeholder-aspect-ratio: 580\/264;\" \/>\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<div class=\"elementor-element elementor-element-1ed9a98 elementor-widget elementor-widget-text-editor\" data-id=\"1ed9a98\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3>Datenaufnahme<\/h3><p>Identifizieren Sie, wo Ihre kontextuellen Daten gespeichert sind, z. B. in Notion, Google Drive, Slack, Salesforce usw. Erstellen Sie Mechanismen, um sowohl bestehende Daten als auch Updates zu erfassen.<\/p><h3>Datenchunking und Embedding<\/h3><p>Die meisten kontextuellen Daten sind unstrukturiert. Verwenden Sie Chunking-Strategien und generieren Sie Embeddings, um die Daten f\u00fcr \u00c4hnlichkeitssuchen zu vektorisieren.<\/p><h3>Speichern und Abrufen von Daten<\/h3><p>Speichern Sie Embeddings in einer Vektordatenbank f\u00fcr einen schnellen Abruf. F\u00fchren Sie zur Laufzeit \u00c4hnlichkeitssuchen durch, um relevante Datenchunks abzurufen und in Prompts einzuf\u00fcgen.<\/p><h3>Sicherheit und Berechtigungen<\/h3><p>Stellen Sie sicheren Speicher und angemessene Berechtigungen sicher, um Datenlecks zu verhindern. Ziehen Sie in Betracht, Enterprise-Level LLMs zu verwenden oder separate Instanzen f\u00fcr jeden Kunden bereitzustellen, um die Sicherheit zu erh\u00f6hen.<\/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<div class=\"elementor-element elementor-element-9c05169 e-flex e-con-boxed e-con e-parent\" data-id=\"9c05169\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-519b5f0 elementor-widget elementor-widget-text-editor\" data-id=\"519b5f0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2><strong>Feinabstimmungsprozess<\/strong><\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-66cfe47 elementor-widget elementor-widget-image\" data-id=\"66cfe47\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" data-src=\"https:\/\/nextbrain.ai\/wp-content\/uploads\/2024\/07\/Fine-Tuning.png\" title=\"Feinabstimmung\" alt=\"Feinabstimmung\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" style=\"--smush-placeholder-width: 770px; --smush-placeholder-aspect-ratio: 770\/350;\" \/>\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<div class=\"elementor-element elementor-element-d567d50 elementor-widget elementor-widget-text-editor\" data-id=\"d567d50\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h3>Datenaufnahme und -vorbereitung<\/h3><p>Nehmen Sie Daten aus externen Anwendungen auf und bereiten Sie saubere Trainingsdatens\u00e4tze vor. Validieren Sie diese Datens\u00e4tze, um qualitativ hochwertige Eingaben sicherzustellen.<\/p><h3>Training und Validierung<\/h3><p>Feinjustieren Sie das Modell mit den vorbereiteten Datens\u00e4tzen. Validieren Sie das Modell, um sicherzustellen, dass es die Leistungsanforderungen vor der Bereitstellung erf\u00fcllt.<\/p><h3>Verst\u00e4rkendes Lernen<\/h3><p>Implementieren Sie Verst\u00e4rkungslernschleifen in der Produktion, um das Modell kontinuierlich mit Nutzerfeedback zu verbessern.<\/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<div class=\"elementor-element elementor-element-b361ecc e-flex e-con-boxed e-con e-parent\" data-id=\"b361ecc\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-bea0501 elementor-widget elementor-widget-text-editor\" data-id=\"bea0501\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Sowohl RAG als auch Feinabstimmung sind wertvoll f\u00fcr die Integration externer Daten zur Verbesserung der LLM-Ausgaben. Angesichts der Komplexit\u00e4t beim Erstellen robuster Trainingsdatens\u00e4tze ist es im Allgemeinen vorteilhafter, mit RAG zu beginnen. In vielen F\u00e4llen kann jedoch die Kombination beider Ans\u00e4tze erforderlich werden.<\/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<div class=\"elementor-element elementor-element-402595c e-flex e-con-boxed e-con e-parent\" data-id=\"402595c\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4348e57 elementor-widget elementor-widget-text-editor\" data-id=\"4348e57\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Bei\u00a0<a href=\"http:\/\/nextbrain.ai\/de\/\">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.\u00a0<a href=\"http:\/\/nextbrain.ai\/de\/schedule-your-free-demo\/\">Vereinbaren Sie noch heute Ihre Demo<\/a>\u00a0um 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<div class=\"elementor-element elementor-element-fcdff78 e-flex e-con-boxed e-con e-parent\" data-id=\"fcdff78\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8beda9c elementor-widget elementor-widget-image\" data-id=\"8beda9c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/nextbrain.ai\/de\/schedule-your-free-demo \/\">\n\t\t\t\t\t\t\t<img decoding=\"async\" data-src=\"https:\/\/nextbrain.ai\/wp-content\/uploads\/2024\/03\/Book-A-Demo.png\" title=\"Demo buchen\" alt=\"Demo buchen\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" style=\"--smush-placeholder-width: 1495px; --smush-placeholder-aspect-ratio: 1495\/120;\" \/>\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":"<p>Let\u2019s say you need to build an AI customer service chatbot. Even if your model is fine-tuned with a specific training dataset, it would be [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":8193,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[70],"tags":[701,703,702,698,704,705,699,587,700,599],"class_list":["post-8192","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-ai-saas","tag-contextual-data","tag-data-ingestion","tag-fine-tuning","tag-large-language-models","tag-llm-optimization","tag-multi-tenant-ai","tag-rag","tag-retrieval-augmented-generation","tag-vector-database"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Fine-tuning or RAG: What\u2019s the Best Approach - NextBrain AI | No-Code Machine Learning<\/title>\n<meta name=\"description\" content=\"Explore the differences between Retrieval Augmented Generation (RAG) and fine-tuning to optimize LLMs by integrating contextual data effectively.\" \/>\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\/fine-tuning-or-rag-whats-the-best-approach\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Fine-tuning or RAG: What\u2019s the Best Approach - 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