{"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\/fr\/blog\/fine-tuning-or-rag-whats-the-best-approach","title":{"rendered":"Fine-tuning ou RAG : Quelle est la meilleure approche"},"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\">Disons que vous devez cr\u00e9er un chatbot de service client AI. M\u00eame si votre model est affin\u00e9 avec un ensemble de donn\u00e9es d'entra\u00eenement sp\u00e9cifique, il serait inefficace sans acc\u00e8s \u00e0 des donn\u00e9es telles que des conversations pass\u00e9es ou des informations sur les produits stock\u00e9es dans les CRMs, documents ou syst\u00e8mes de tickets des clients.<\/p><p class=\"p1\">Pour utiliser ces donn\u00e9es contextuelles, vous devez les int\u00e9grer \u00e0 vos LLMs. Cela implique l'ingestion de donn\u00e9es provenant de sources tierces et le choix entre RAG et le fine-tuning pour utiliser les donn\u00e9es efficacement.<\/p><p>Mais quelle est la meilleure approche : le fine-tuning ou la Retrieval Augmented Generation (RAG) ? Cet article fournit une comparaison d\u00e9taill\u00e9e entre les deux.<\/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>G\u00e9n\u00e9ration Augment\u00e9e par R\u00e9cup\u00e9ration (RAG)<\/h3><p>RAG am\u00e9liore la pr\u00e9cision des LLMs en r\u00e9cup\u00e9rant des donn\u00e9es externes \u00e0 la demande et en injectant du contexte dans les prompts en temps r\u00e9el. Ces donn\u00e9es peuvent provenir de diverses sources telles que la documentation client, les pages web et des applications tierces comme les CRMs et Google Drive.<\/p><h4>Composants Cl\u00e9s de RAG<\/h4><ol><li><p><strong>Ingestion et Stockage des Donn\u00e9es<\/strong>:<\/p><ul><li><strong>Ingestion Initiale<\/strong>: R\u00e9cup\u00e9rez toutes les donn\u00e9es clients pertinentes au d\u00e9part.<\/li><li><strong>Mises \u00e0 Jour Continues<\/strong>: Utilisez des t\u00e2ches en arri\u00e8re-plan pour garder les donn\u00e9es \u00e0 jour en temps r\u00e9el.<\/li><li><strong>Embeddings et Stockage<\/strong>: Stockez les donn\u00e9es dans une base de donn\u00e9es vectorielle pour la r\u00e9cup\u00e9ration.<\/li><\/ul><\/li><li><p><strong>Injection de prompt<\/strong>:<\/p><ul><li><strong>\u00c0 l'ex\u00e9cution<\/strong>: R\u00e9cup\u00e9rer des morceaux de texte pertinents de la base de donn\u00e9es vectorielle et les injecter dans le prompt\/requ\u00eate initial pour que le LLM g\u00e9n\u00e8re la r\u00e9ponse finale.<\/li><\/ul><\/li><\/ol><h3>Ajustement fin<\/h3><p>L'ajustement fin implique un entra\u00eenement suppl\u00e9mentaire d'un LLM pr\u00e9-entra\u00een\u00e9 sur un ensemble de donn\u00e9es sp\u00e9cifique \u00e0 un domaine pour am\u00e9liorer ses performances sur des t\u00e2ches sp\u00e9cifiques. Par exemple, ajuster un mod\u00e8le sur des e-mails de vente pour cr\u00e9er un agent commercial IA.<\/p><h4>D\u00e9fis de l'ajustement fin<\/h4><ul><li><strong>Pr\u00e9paration des donn\u00e9es<\/strong>: N\u00e9cessite un ensemble de donn\u00e9es d'entra\u00eenement propre et bien structur\u00e9.<\/li><li><strong>R\u00e9sultats pr\u00e9visibles<\/strong>: Produit des r\u00e9sultats plus pr\u00e9visibles mais est chronophage.<\/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 : Lequel choisir ?<\/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>Quand utiliser RAG<\/h3><ul><li>Injecte un contexte en temps r\u00e9el dans les invites.<\/li><li>Ne n\u00e9cessite pas de jeu de donn\u00e9es d'entra\u00eenement structur\u00e9.<\/li><li>R\u00e9cup\u00e8re un contexte pertinent \u00e0 partir de plusieurs sources de donn\u00e9es.<\/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>Quand utiliser le Fine-Tuning<\/h3><ul><li>Lorsque vous avez un jeu de donn\u00e9es sp\u00e9cifique et bien pr\u00e9par\u00e9 pour l'entra\u00eenement.<\/li><li>Pour des t\u00e2ches n\u00e9cessitant des r\u00e9sultats pr\u00e9visibles.<\/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>Mise en \u0153uvre de 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>Ingestion de donn\u00e9es<\/h3><p>Identifiez o\u00f9 vos donn\u00e9es contextuelles r\u00e9sident, par exemple dans Notion, Google Drive, Slack, Salesforce, etc. Cr\u00e9ez des m\u00e9canismes pour ing\u00e9rer \u00e0 la fois les donn\u00e9es existantes et les mises \u00e0 jour.<\/p><h3>D\u00e9coupage et Embedding des Donn\u00e9es<\/h3><p>La plupart des donn\u00e9es contextuelles sont non structur\u00e9es. Utilisez des strat\u00e9gies de d\u00e9coupage et g\u00e9n\u00e9rez des embeddings pour vectoriser les donn\u00e9es pour les recherches de similarit\u00e9.<\/p><h3>Stockage et R\u00e9cup\u00e9ration des Donn\u00e9es<\/h3><p>Stockez les embeddings dans une base de donn\u00e9es vectorielle pour une r\u00e9cup\u00e9ration rapide. En temps d'ex\u00e9cution, effectuez des recherches de similarit\u00e9 pour r\u00e9cup\u00e9rer des morceaux de donn\u00e9es pertinents et les inclure dans les prompts.<\/p><h3>S\u00e9curit\u00e9 et Permissions<\/h3><p>Assurez-vous d'un stockage s\u00e9curis\u00e9 et de permissions appropri\u00e9es pour pr\u00e9venir les fuites de donn\u00e9es. Envisagez d'utiliser des LLMs de niveau entreprise ou de d\u00e9ployer des instances s\u00e9par\u00e9es pour chaque client afin d'am\u00e9liorer la s\u00e9curit\u00e9.<\/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>Processus de Fine-Tuning<\/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=\"Ajustement Fin\" alt=\"Ajustement Fin\" 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>Ingestion et Pr\u00e9paration des Donn\u00e9es<\/h3><p>Ing\u00e9rez des donn\u00e9es provenant d'applications externes et pr\u00e9parez des ensembles de donn\u00e9es d'entra\u00eenement propres. Validez ces ensembles de donn\u00e9es pour garantir des entr\u00e9es de qualit\u00e9.<\/p><h3>Formation et Validation<\/h3><p>Affinez le model avec les ensembles de donn\u00e9es pr\u00e9par\u00e9s. Validez le model pour vous assurer qu'il r\u00e9pond aux crit\u00e8res de performance avant le d\u00e9ploiement.<\/p><h3>Apprentissage par renforcement<\/h3><p>Impl\u00e9mentez des boucles d'apprentissage par renforcement en production pour am\u00e9liorer continuellement le model en utilisant les retours des utilisateurs.<\/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>\u00c0 la fois RAG et l'affinage sont pr\u00e9cieux pour int\u00e9grer des donn\u00e9es externes afin d'am\u00e9liorer les sorties LLM. \u00c9tant donn\u00e9 les complexit\u00e9s de la cr\u00e9ation d'ensembles de donn\u00e9es d'entra\u00eenement robustes, commencer par RAG est g\u00e9n\u00e9ralement plus b\u00e9n\u00e9fique. Cependant, dans de nombreux cas, combiner les deux approches peut devenir essentiel.<\/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>Chez\u00a0<a href=\"http:\/\/nextbrain.ai\/fr\/\">NextBrain AI<\/a>, nous utilisons la derni\u00e8re technologie AI pour fournir une analyse de donn\u00e9es pr\u00e9cise et des insights commerciaux exploitables, sans les complexit\u00e9s souvent associ\u00e9es aux mises en \u0153uvre techniques.\u00a0<a href=\"http:\/\/nextbrain.ai\/fr\/schedule-your-free-demo\/\">Planifiez votre d\u00e9monstration aujourd'hui<\/a>\u00a0pour exp\u00e9rimenter de premi\u00e8re main comment notre solution fonctionne.<\/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\/fr\/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=\"R\u00e9servez une d\u00e9mo\" alt=\"R\u00e9servez une d\u00e9mo\" 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\/fr\/blog\/fine-tuning-or-rag-whats-the-best-approach\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\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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