{"id":2265,"date":"2026-04-20T10:06:33","date_gmt":"2026-04-20T08:06:33","guid":{"rendered":"https:\/\/askem.eu\/?p=2265"},"modified":"2026-04-20T10:06:37","modified_gmt":"2026-04-20T08:06:37","slug":"rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source","status":"publish","type":"post","link":"https:\/\/askem.eu\/en\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/","title":{"rendered":"RAG ou fine-tuning : comment choisir pour sa stack IA open source"},"content":{"rendered":"

RAG ou fine-tuning : comment choisir pour sa stack IA open source<\/h2>\n\n\n\n

Sur une stack IA auto-h\u00e9berg\u00e9e, deux leviers permettent d’adapter un LLM \u00e0 son contexte m\u00e9tier : injecter de la connaissance au moment de la requ\u00eate (RAG<\/strong>, Retrieval-Augmented Generation<\/em>) ou entra\u00eener le mod\u00e8le sur ses propres donn\u00e9es (fine-tuning<\/strong>). Les deux approches sont souvent pr\u00e9sent\u00e9es comme concurrentes, alors qu’elles r\u00e9pondent \u00e0 des probl\u00e8mes diff\u00e9rents. Cet article propose une matrice de d\u00e9cision concr\u00e8te, pens\u00e9e pour des \u00e9quipes qui construisent leur propre infrastructure, avec des mod\u00e8les comme Mistral, Llama ou Qwen, et des briques comme pgvector<\/a>, Ollama<\/a> ou Unsloth<\/a>.<\/p>\n\n\n\n

Ce que les deux approches font vraiment<\/h3>\n\n\n\n

Le RAG<\/strong> garde le mod\u00e8le tel quel et enrichit sa r\u00e9ponse en r\u00e9cup\u00e9rant dynamiquement, \u00e0 chaque requ\u00eate, les passages pertinents d’une base documentaire. Le mod\u00e8le voit ces passages dans son contexte et produit une r\u00e9ponse fond\u00e9e sur ces sources. L’infrastructure typique associe une base vectorielle (pgvector, Qdrant<\/a>), un mod\u00e8le d’embeddings et un LLM d’inf\u00e9rence.<\/p>\n\n\n\n

Le fine-tuning<\/strong>, \u00e0 l’inverse, modifie les poids du mod\u00e8le. On lui apprend un style, un vocabulaire m\u00e9tier, un format de sortie ou une t\u00e2che sp\u00e9cifique en lui pr\u00e9sentant des milliers d’exemples. Avec des techniques comme LoRA ou QLoRA (port\u00e9es par Unsloth ou Axolotl), on peut ajuster un mod\u00e8le de 7 \u00e0 13 milliards de param\u00e8tres sur un seul GPU grand public.<\/p>\n\n\n\n

Les crit\u00e8res qui tranchent<\/h3>\n\n\n\n

La question n’est pas \u00ab lequel est meilleur \u00bb mais \u00ab quel probl\u00e8me est-ce que je cherche \u00e0 r\u00e9soudre \u00bb. Voici les crit\u00e8res qui, en pratique, orientent la d\u00e9cision :<\/p>\n\n\n\n

    \n
  • La connaissance \u00e0 injecter change-t-elle souvent ?<\/strong> Si oui (jurisprudence, documentation produit, actualit\u00e9), le RAG est incontournable : on met \u00e0 jour la base, pas le mod\u00e8le.<\/li>\n\n\n\n
  • A-t-on besoin de sources citables ?<\/strong> Le RAG fournit nativement les passages utilis\u00e9s. Un mod\u00e8le fine-tun\u00e9, non : il \u00ab sait \u00bb mais ne peut pas prouver.<\/li>\n\n\n\n
  • Le probl\u00e8me est-il un d\u00e9faut de format, de ton ou de comportement ?<\/strong> L\u00e0, le fine-tuning est plus efficace. Apprendre \u00e0 un mod\u00e8le \u00e0 produire syst\u00e9matiquement du JSON valide, \u00e0 r\u00e9pondre dans un ton institutionnel, ou \u00e0 suivre un processus m\u00e9tier sp\u00e9cifique passe mal par le prompt.<\/li>\n\n\n\n
  • Les donn\u00e9es sont-elles volumineuses et statiques ?<\/strong> Un corpus de dix mille documents techniques stables peut \u00eatre fine-tun\u00e9 avec b\u00e9n\u00e9fice. Le m\u00eame corpus mis \u00e0 jour chaque semaine ne le sera pas.<\/li>\n\n\n\n
  • Quelle est la latence acceptable ?<\/strong> Un RAG ajoute un aller-retour de recherche vectorielle et une fen\u00eatre de contexte plus longue. Un mod\u00e8le fine-tun\u00e9 r\u00e9pond en une seule passe, souvent plus vite.<\/li>\n\n\n\n
  • Quelle est la taille d’\u00e9quipe disponible ?<\/strong> Le RAG est plus facile \u00e0 maintenir (remplacer des documents, r\u00e9indexer). Le fine-tuning demande des comp\u00e9tences ML, une pipeline d’entra\u00eenement, une \u00e9valuation syst\u00e9matique.<\/li>\n<\/ul>\n\n\n\n

    Une matrice de d\u00e9cision simple<\/h3>\n\n\n\n
    Besoin<\/th>Approche recommand\u00e9e<\/th><\/tr><\/thead>
    R\u00e9pondre \u00e0 partir d’une documentation qui \u00e9volue<\/td>RAG<\/td><\/tr>
    Produire des sorties au format impos\u00e9 (JSON, XML, gabarits)<\/td>Fine-tuning (ou grammaire contrainte type Outlines)<\/td><\/tr>
    Imiter un style r\u00e9dactionnel m\u00e9tier<\/td>Fine-tuning<\/td><\/tr>
    R\u00e9pondre avec sources citables<\/td>RAG<\/td><\/tr>
    Faire baisser le co\u00fbt d’inf\u00e9rence sur un cas tr\u00e8s r\u00e9p\u00e9titif<\/td>Fine-tuning d’un petit mod\u00e8le<\/td><\/tr>
    Ma\u00eetriser un jargon m\u00e9tier rare<\/td>Fine-tuning + RAG (les deux)<\/td><\/tr>
    Prototyper rapidement un assistant documentaire<\/td>RAG<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

    Le pi\u00e8ge classique : fine-tuner pour injecter de la connaissance<\/h3>\n\n\n\n

    C’est l’erreur la plus fr\u00e9quente. Une \u00e9quipe veut qu’un mod\u00e8le \u00ab connaisse \u00bb sa documentation interne, lance un fine-tuning, et constate que le mod\u00e8le hallucine autant qu’avant. Le fine-tuning n’est pas un m\u00e9canisme fiable pour stocker des faits pr\u00e9cis : il apprend des r\u00e9gularit\u00e9s statistiques, pas un index. R\u00e9sultat : le mod\u00e8le confond des r\u00e9f\u00e9rences, invente des chiffres, m\u00e9lange des proc\u00e9dures. Pour transmettre de la connaissance, le RAG reste la bonne voie. Le fine-tuning sert \u00e0 conditionner le comportement<\/em>, pas \u00e0 remplir la m\u00e9moire<\/em>.<\/p>\n\n\n\n

    La combinaison gagnante<\/h3>\n\n\n\n

    Dans les architectures matures, on empile souvent les deux. Un mod\u00e8le de base (Llama 3 ou Mistral, par exemple) est fine-tun\u00e9 l\u00e9g\u00e8rement pour adopter un style, un format de r\u00e9ponse et une politique de refus adapt\u00e9e. Puis ce mod\u00e8le est servi derri\u00e8re un RAG qui lui injecte la connaissance sp\u00e9cifique \u00e0 chaque requ\u00eate. On obtient ainsi un assistant qui parle le bon langage et qui s’appuie sur des sources \u00e0 jour. C’est cette combinaison qui permet d’atteindre un niveau de qualit\u00e9 comparable aux grandes API propri\u00e9taires, sur une infrastructure auto-h\u00e9berg\u00e9e.<\/p>\n\n\n\n

    Par o\u00f9 commencer concr\u00e8tement<\/h3>\n\n\n\n

    Dans 80 % des cas d’usage d’une organisation (assistance documentaire, questions-r\u00e9ponses internes, analyse de contrats, synth\u00e8se de dossiers), un RAG bien construit suffit<\/strong> et rend des r\u00e9sultats d\u00e8s la premi\u00e8re semaine. Le fine-tuning ne devient int\u00e9ressant qu’une fois le RAG en place, quand on a identifi\u00e9 pr\u00e9cis\u00e9ment les d\u00e9fauts r\u00e9siduels : un ton \u00e0 ajuster, un format \u00e0 fiabiliser, un vocabulaire \u00e0 faire acqu\u00e9rir. Ouvrir un chantier de fine-tuning avant d’avoir un RAG fonctionnel, c’est tr\u00e8s souvent r\u00e9soudre le mauvais probl\u00e8me.<\/p>\n\n\n\n

    C\u00f4t\u00e9 outils open source, la combinaison typique aujourd’hui reste : Ollama ou vLLM<\/strong> pour servir le mod\u00e8le, pgvector ou Qdrant<\/strong> pour la base vectorielle, un mod\u00e8le d’embeddings<\/strong> type BGE ou Nomic, LangGraph<\/strong> ou Dify<\/strong> pour orchestrer, et Unsloth<\/strong> le jour o\u00f9 le fine-tuning devient pertinent. Commencer simple, mesurer, puis d\u00e9cider de monter en complexit\u00e9 : c’est la feuille de route qui \u00e9vite les impasses.<\/p>\n\n\n\n

    <\/p>","protected":false},"excerpt":{"rendered":"

    RAG ou fine-tuning : comment choisir pour sa stack IA open source Sur une stack IA auto-h\u00e9berg\u00e9e, deux leviers permettent d’adapter un LLM \u00e0 son contexte m\u00e9tier : injecter de la connaissance au moment de la requ\u00eate (RAG, Retrieval-Augmented Generation) ou entra\u00eener le mod\u00e8le sur ses propres donn\u00e9es (fine-tuning). Les deux approches sont souvent pr\u00e9sent\u00e9es comme concurrentes, […]<\/p>\n","protected":false},"author":1,"featured_media":2266,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"ocean_post_layout":"","ocean_both_sidebars_style":"","ocean_both_sidebars_content_width":0,"ocean_both_sidebars_sidebars_width":0,"ocean_sidebar":"","ocean_second_sidebar":"","ocean_disable_margins":"enable","ocean_add_body_class":"","ocean_shortcode_before_top_bar":"","ocean_shortcode_after_top_bar":"","ocean_shortcode_before_header":"","ocean_shortcode_after_header":"","ocean_has_shortcode":"","ocean_shortcode_after_title":"","ocean_shortcode_before_footer_widgets":"","ocean_shortcode_after_footer_widgets":"","ocean_shortcode_before_footer_bottom":"","ocean_shortcode_after_footer_bottom":"","ocean_display_top_bar":"default","ocean_display_header":"default","ocean_header_style":"","ocean_center_header_left_menu":"","ocean_custom_header_template":"","ocean_custom_logo":0,"ocean_custom_retina_logo":0,"ocean_custom_logo_max_width":0,"ocean_custom_logo_tablet_max_width":0,"ocean_custom_logo_mobile_max_width":0,"ocean_custom_logo_max_height":0,"ocean_custom_logo_tablet_max_height":0,"ocean_custom_logo_mobile_max_height":0,"ocean_header_custom_menu":"","ocean_menu_typo_font_family":"","ocean_menu_typo_font_subset":"","ocean_menu_typo_font_size":0,"ocean_menu_typo_font_size_tablet":0,"ocean_menu_typo_font_size_mobile":0,"ocean_menu_typo_font_size_unit":"px","ocean_menu_typo_font_weight":"","ocean_menu_typo_font_weight_tablet":"","ocean_menu_typo_font_weight_mobile":"","ocean_menu_typo_transform":"","ocean_menu_typo_transform_tablet":"","ocean_menu_typo_transform_mobile":"","ocean_menu_typo_line_height":0,"ocean_menu_typo_line_height_tablet":0,"ocean_menu_typo_line_height_mobile":0,"ocean_menu_typo_line_height_unit":"","ocean_menu_typo_spacing":0,"ocean_menu_typo_spacing_tablet":0,"ocean_menu_typo_spacing_mobile":0,"ocean_menu_typo_spacing_unit":"","ocean_menu_link_color":"","ocean_menu_link_color_hover":"","ocean_menu_link_color_active":"","ocean_menu_link_background":"","ocean_menu_link_hover_background":"","ocean_menu_link_active_background":"","ocean_menu_social_links_bg":"","ocean_menu_social_hover_links_bg":"","ocean_menu_social_links_color":"","ocean_menu_social_hover_links_color":"","ocean_disable_title":"default","ocean_disable_heading":"default","ocean_post_title":"","ocean_post_subheading":"","ocean_post_title_style":"","ocean_post_title_background_color":"","ocean_post_title_background":0,"ocean_post_title_bg_image_position":"","ocean_post_title_bg_image_attachment":"","ocean_post_title_bg_image_repeat":"","ocean_post_title_bg_image_size":"","ocean_post_title_height":0,"ocean_post_title_bg_overlay":0.5,"ocean_post_title_bg_overlay_color":"","ocean_disable_breadcrumbs":"default","ocean_breadcrumbs_color":"","ocean_breadcrumbs_separator_color":"","ocean_breadcrumbs_links_color":"","ocean_breadcrumbs_links_hover_color":"","ocean_display_footer_widgets":"default","ocean_display_footer_bottom":"default","ocean_custom_footer_template":"","osh_disable_topbar_sticky":"default","osh_disable_header_sticky":"default","osh_sticky_header_style":"default","osh_sticky_header_effect":"","osh_custom_sticky_logo":0,"osh_custom_retina_sticky_logo":0,"osh_custom_sticky_logo_height":0,"osh_background_color":"","osh_links_color":"","osh_links_hover_color":"","osh_links_active_color":"","osh_links_bg_color":"","osh_links_hover_bg_color":"","osh_links_active_bg_color":"","osh_menu_social_links_color":"","osh_menu_social_hover_links_color":"","ocean_post_oembed":"","ocean_post_self_hosted_media":"","ocean_post_video_embed":"","ocean_link_format":"","ocean_link_format_target":"self","ocean_quote_format":"","ocean_quote_format_link":"post","ocean_gallery_link_images":"on","ocean_gallery_id":[],"footnotes":""},"categories":[16],"tags":[],"class_list":["post-2265","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","entry","has-media"],"yoast_head":"\nRAG ou fine-tuning : comment choisir pour sa stack IA open source - askem<\/title>\n<meta name=\"description\" content=\"ASKEM BUREAU D'\u00c9TUDES ET DE FORMATION NUM\u00c9RIQUE. Nous vous assistons dans la transformation num\u00e9rique de vos outils, services et organisations tout en pla\u00e7ant l\u2019humain au c\u0153ur de notre d\u00e9marche d\u2019accompagnement.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/askem.eu\/en\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"RAG ou fine-tuning : comment choisir pour sa stack IA open source - askem\" \/>\n<meta property=\"og:description\" content=\"ASKEM BUREAU D'\u00c9TUDES ET DE FORMATION NUM\u00c9RIQUE. Nous vous assistons dans la transformation num\u00e9rique de vos outils, services et organisations tout en pla\u00e7ant l\u2019humain au c\u0153ur de notre d\u00e9marche d\u2019accompagnement.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/askem.eu\/en\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/\" \/>\n<meta property=\"og:site_name\" content=\"askem\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/fb.me\/askem.eu\" \/>\n<meta property=\"article:published_time\" content=\"2026-04-20T08:06:33+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-04-20T08:06:37+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/mlpi0fxo3sth.i.optimole.com\/cb:3obA.c61\/w:auto\/h:auto\/q:mauto\/f:best\/https:\/\/askem.eu\/wp-content\/uploads\/2026\/04\/sujet-askem-2026-04-18.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1600\" \/>\n\t<meta property=\"og:image:height\" content=\"1000\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"askemadmin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"askemadmin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/askem.eu\\\/2026\\\/04\\\/20\\\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/askem.eu\\\/2026\\\/04\\\/20\\\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\\\/\"},\"author\":{\"name\":\"askemadmin\",\"@id\":\"https:\\\/\\\/askem.eu\\\/#\\\/schema\\\/person\\\/8bbee74ab9a977d56bf4826662e9d2e9\"},\"headline\":\"RAG ou fine-tuning : comment choisir pour sa stack IA open source\",\"datePublished\":\"2026-04-20T08:06:33+00:00\",\"dateModified\":\"2026-04-20T08:06:37+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/askem.eu\\\/2026\\\/04\\\/20\\\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\\\/\"},\"wordCount\":964,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/askem.eu\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/askem.eu\\\/2026\\\/04\\\/20\\\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\/\\/askem.eu\\/wp-content\\/uploads\\/2026\\/04\\/sujet-askem-2026-04-18.png\",\"articleSection\":[\"AI\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/askem.eu\\\/2026\\\/04\\\/20\\\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/askem.eu\\\/2026\\\/04\\\/20\\\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\\\/\",\"url\":\"https:\\\/\\\/askem.eu\\\/2026\\\/04\\\/20\\\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\\\/\",\"name\":\"RAG ou fine-tuning : comment choisir pour sa stack IA open source - askem\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/askem.eu\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/askem.eu\\\/2026\\\/04\\\/20\\\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/askem.eu\\\/2026\\\/04\\\/20\\\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\/\\/askem.eu\\/wp-content\\/uploads\\/2026\\/04\\/sujet-askem-2026-04-18.png\",\"datePublished\":\"2026-04-20T08:06:33+00:00\",\"dateModified\":\"2026-04-20T08:06:37+00:00\",\"description\":\"ASKEM BUREAU D'\u00c9TUDES ET DE FORMATION NUM\u00c9RIQUE. Nous vous assistons dans la transformation num\u00e9rique de vos outils, services et organisations tout en pla\u00e7ant l\u2019humain au c\u0153ur de notre d\u00e9marche d\u2019accompagnement.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/askem.eu\\\/2026\\\/04\\\/20\\\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/askem.eu\\\/2026\\\/04\\\/20\\\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/askem.eu\\\/2026\\\/04\\\/20\\\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\\\/#primaryimage\",\"url\":\"https:\\/\\/askem.eu\\/wp-content\\/uploads\\/2026\\/04\\/sujet-askem-2026-04-18.png\",\"contentUrl\":\"https:\\/\\/askem.eu\\/wp-content\\/uploads\\/2026\\/04\\/sujet-askem-2026-04-18.png\",\"width\":1600,\"height\":1000},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/askem.eu\\\/2026\\\/04\\\/20\\\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Accueil\",\"item\":\"https:\\\/\\\/askem.eu\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"RAG ou fine-tuning : comment choisir pour sa stack IA open source\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/askem.eu\\\/#website\",\"url\":\"https:\\\/\\\/askem.eu\\\/\",\"name\":\"askem\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\\\/\\\/askem.eu\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/askem.eu\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/askem.eu\\\/#organization\",\"name\":\"Askem\",\"url\":\"https:\\\/\\\/askem.eu\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/askem.eu\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\/\\/mlpi0fxo3sth.i.optimole.com\\/cb:3obA.c61\\/w:760\\/h:480\\/q:mauto\\/f:best\\/https:\\/\\/askem.eu\\/wp-content\\/uploads\\/2020\\/10\\/logoGalaxieAskem3.png\",\"contentUrl\":\"https:\\/\\/mlpi0fxo3sth.i.optimole.com\\/cb:3obA.c61\\/w:760\\/h:480\\/q:mauto\\/f:best\\/https:\\/\\/askem.eu\\/wp-content\\/uploads\\/2020\\/10\\/logoGalaxieAskem3.png\",\"width\":760,\"height\":480,\"caption\":\"Askem\"},\"image\":{\"@id\":\"https:\\\/\\\/askem.eu\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/fb.me\\\/askem.eu\",\"https:\\\/\\\/linkedin.com\\\/company\\\/askem-eu\"]},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/askem.eu\\\/#\\\/schema\\\/person\\\/8bbee74ab9a977d56bf4826662e9d2e9\",\"name\":\"askemadmin\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/a202f744ee3a4b6fdbe2ceb57fd84c72559337791a276662270d8d2fb7842e3f?s=96&d=mm&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/a202f744ee3a4b6fdbe2ceb57fd84c72559337791a276662270d8d2fb7842e3f?s=96&d=mm&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/a202f744ee3a4b6fdbe2ceb57fd84c72559337791a276662270d8d2fb7842e3f?s=96&d=mm&r=g\",\"caption\":\"askemadmin\"},\"sameAs\":[\"https:\\\/\\\/askem.eu\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"RAG ou fine-tuning : comment choisir pour sa stack IA open source - askem","description":"ASKEM BUREAU D'\u00c9TUDES ET DE FORMATION NUM\u00c9RIQUE. Nous vous assistons dans la transformation num\u00e9rique de vos outils, services et organisations tout en pla\u00e7ant l\u2019humain au c\u0153ur de notre d\u00e9marche d\u2019accompagnement.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/askem.eu\/en\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/","og_locale":"en_US","og_type":"article","og_title":"RAG ou fine-tuning : comment choisir pour sa stack IA open source - askem","og_description":"ASKEM BUREAU D'\u00c9TUDES ET DE FORMATION NUM\u00c9RIQUE. Nous vous assistons dans la transformation num\u00e9rique de vos outils, services et organisations tout en pla\u00e7ant l\u2019humain au c\u0153ur de notre d\u00e9marche d\u2019accompagnement.","og_url":"https:\/\/askem.eu\/en\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/","og_site_name":"askem","article_publisher":"https:\/\/fb.me\/askem.eu","article_published_time":"2026-04-20T08:06:33+00:00","article_modified_time":"2026-04-20T08:06:37+00:00","og_image":[{"width":1600,"height":1000,"url":"https:\/\/mlpi0fxo3sth.i.optimole.com\/cb:3obA.c61\/w:auto\/h:auto\/q:mauto\/f:best\/https:\/\/askem.eu\/wp-content\/uploads\/2026\/04\/sujet-askem-2026-04-18.png","type":"image\/png"}],"author":"askemadmin","twitter_card":"summary_large_image","twitter_misc":{"Written by":"askemadmin","Est. reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/askem.eu\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/#article","isPartOf":{"@id":"https:\/\/askem.eu\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/"},"author":{"name":"askemadmin","@id":"https:\/\/askem.eu\/#\/schema\/person\/8bbee74ab9a977d56bf4826662e9d2e9"},"headline":"RAG ou fine-tuning : comment choisir pour sa stack IA open source","datePublished":"2026-04-20T08:06:33+00:00","dateModified":"2026-04-20T08:06:37+00:00","mainEntityOfPage":{"@id":"https:\/\/askem.eu\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/"},"wordCount":964,"commentCount":0,"publisher":{"@id":"https:\/\/askem.eu\/#organization"},"image":{"@id":"https:\/\/askem.eu\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/#primaryimage"},"thumbnailUrl":"https:\/\/mlpi0fxo3sth.i.optimole.com\/cb:3obA.c61\/w:auto\/h:auto\/q:mauto\/f:best\/https:\/\/askem.eu\/wp-content\/uploads\/2026\/04\/sujet-askem-2026-04-18.png","articleSection":["AI"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/askem.eu\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/askem.eu\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/","url":"https:\/\/askem.eu\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/","name":"RAG ou fine-tuning : comment choisir pour sa stack IA open source - askem","isPartOf":{"@id":"https:\/\/askem.eu\/#website"},"primaryImageOfPage":{"@id":"https:\/\/askem.eu\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/#primaryimage"},"image":{"@id":"https:\/\/askem.eu\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/#primaryimage"},"thumbnailUrl":"https:\/\/mlpi0fxo3sth.i.optimole.com\/cb:3obA.c61\/w:auto\/h:auto\/q:mauto\/f:best\/https:\/\/askem.eu\/wp-content\/uploads\/2026\/04\/sujet-askem-2026-04-18.png","datePublished":"2026-04-20T08:06:33+00:00","dateModified":"2026-04-20T08:06:37+00:00","description":"ASKEM BUREAU D'\u00c9TUDES ET DE FORMATION NUM\u00c9RIQUE. Nous vous assistons dans la transformation num\u00e9rique de vos outils, services et organisations tout en pla\u00e7ant l\u2019humain au c\u0153ur de notre d\u00e9marche d\u2019accompagnement.","breadcrumb":{"@id":"https:\/\/askem.eu\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/askem.eu\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/askem.eu\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/#primaryimage","url":"https:\/\/mlpi0fxo3sth.i.optimole.com\/cb:3obA.c61\/w:auto\/h:auto\/q:mauto\/f:best\/https:\/\/askem.eu\/wp-content\/uploads\/2026\/04\/sujet-askem-2026-04-18.png","contentUrl":"https:\/\/mlpi0fxo3sth.i.optimole.com\/cb:3obA.c61\/w:auto\/h:auto\/q:mauto\/f:best\/https:\/\/askem.eu\/wp-content\/uploads\/2026\/04\/sujet-askem-2026-04-18.png","width":1600,"height":1000},{"@type":"BreadcrumbList","@id":"https:\/\/askem.eu\/2026\/04\/20\/rag-ou-fine-tuning-comment-choisir-pour-sa-stack-ia-open-source\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Accueil","item":"https:\/\/askem.eu\/"},{"@type":"ListItem","position":2,"name":"RAG ou fine-tuning : comment choisir pour sa stack IA open source"}]},{"@type":"WebSite","@id":"https:\/\/askem.eu\/#website","url":"https:\/\/askem.eu\/","name":"askem","description":"","publisher":{"@id":"https:\/\/askem.eu\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/askem.eu\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/askem.eu\/#organization","name":"Askem","url":"https:\/\/askem.eu\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/askem.eu\/#\/schema\/logo\/image\/","url":"https:\/\/mlpi0fxo3sth.i.optimole.com\/cb:3obA.c61\/w:760\/h:480\/q:mauto\/f:best\/https:\/\/askem.eu\/wp-content\/uploads\/2020\/10\/logoGalaxieAskem3.png","contentUrl":"https:\/\/mlpi0fxo3sth.i.optimole.com\/cb:3obA.c61\/w:760\/h:480\/q:mauto\/f:best\/https:\/\/askem.eu\/wp-content\/uploads\/2020\/10\/logoGalaxieAskem3.png","width":760,"height":480,"caption":"Askem"},"image":{"@id":"https:\/\/askem.eu\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/fb.me\/askem.eu","https:\/\/linkedin.com\/company\/askem-eu"]},{"@type":"Person","@id":"https:\/\/askem.eu\/#\/schema\/person\/8bbee74ab9a977d56bf4826662e9d2e9","name":"askemadmin","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/a202f744ee3a4b6fdbe2ceb57fd84c72559337791a276662270d8d2fb7842e3f?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/a202f744ee3a4b6fdbe2ceb57fd84c72559337791a276662270d8d2fb7842e3f?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/a202f744ee3a4b6fdbe2ceb57fd84c72559337791a276662270d8d2fb7842e3f?s=96&d=mm&r=g","caption":"askemadmin"},"sameAs":["https:\/\/askem.eu"]}]}},"_links":{"self":[{"href":"https:\/\/askem.eu\/en\/wp-json\/wp\/v2\/posts\/2265","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/askem.eu\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/askem.eu\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/askem.eu\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/askem.eu\/en\/wp-json\/wp\/v2\/comments?post=2265"}],"version-history":[{"count":1,"href":"https:\/\/askem.eu\/en\/wp-json\/wp\/v2\/posts\/2265\/revisions"}],"predecessor-version":[{"id":2267,"href":"https:\/\/askem.eu\/en\/wp-json\/wp\/v2\/posts\/2265\/revisions\/2267"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/askem.eu\/en\/wp-json\/wp\/v2\/media\/2266"}],"wp:attachment":[{"href":"https:\/\/askem.eu\/en\/wp-json\/wp\/v2\/media?parent=2265"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/askem.eu\/en\/wp-json\/wp\/v2\/categories?post=2265"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/askem.eu\/en\/wp-json\/wp\/v2\/tags?post=2265"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}