{"id":208,"date":"2025-01-31T06:09:00","date_gmt":"2025-05-30T21:23:32","guid":{"rendered":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/p\/definition_prompt-chaining\/"},"modified":"2025-06-05T23:33:41","modified_gmt":"2025-06-05T21:33:41","slug":"definition-prompt-chaining","status":"publish","type":"post","link":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/p\/definition-prompt-chaining\/","title":{"rendered":"Prompt chaining"},"content":{"rendered":"<p>Le <em>prompt chaining<\/em> est une technique avanc\u00e9e en <em>prompt engineering<\/em>, un domaine crucial pour interagir efficacement avec les mod\u00e8les d&rsquo;intelligence artificielle.  Qu&rsquo;est-ce que prompt chaining ? C&rsquo;est le fait d&rsquo;encha\u00eener plusieurs prompts pour d\u00e9composer une t\u00e2che complexe en sous-t\u00e2ches plus simples.<\/p>\n<h3>Comment fonctionne prompt chaining ?<\/h3>\n<p>Au lieu de soumettre un seul prompt complexe \u00e0 un mod\u00e8le d&rsquo;IA, vous le d\u00e9composez en une s\u00e9rie de prompts plus petits et plus pr\u00e9cis. La sortie de chaque prompt sert d&rsquo;entr\u00e9e pour le suivant, cr\u00e9ant ainsi une cha\u00eene. Imaginez une ligne d&rsquo;assemblage\u00a0: chaque \u00e9tape (prompt) transforme la mati\u00e8re premi\u00e8re (l&rsquo;information) jusqu&rsquo;\u00e0 obtenir le produit final (le r\u00e9sultat souhait\u00e9).  Le premier prompt peut, par exemple, extraire des informations d&rsquo;un texte, le deuxi\u00e8me les r\u00e9sumer, et le troisi\u00e8me les traduire dans une autre langue.<\/p>\n<h3>Pourquoi prompt chaining est-il important\u00a0?<\/h3>\n<p>Le prompt chaining est essentiel pour traiter des requ\u00eates complexes qui seraient difficiles \u00e0 g\u00e9rer avec un seul prompt.  Il permet d&rsquo;am\u00e9liorer la pr\u00e9cision et la pertinence des r\u00e9ponses en guidant le mod\u00e8le \u00e9tape par \u00e9tape.  En d\u00e9composant le probl\u00e8me, vous r\u00e9duisez la charge cognitive du mod\u00e8le et augmentez ses chances de succ\u00e8s. Par exemple, pour analyser un rapport financier, un premier prompt pourrait extraire les chiffres cl\u00e9s, un second calculer des ratios, et un troisi\u00e8me r\u00e9diger un r\u00e9sum\u00e9 de la performance.<\/p>\n<h3>Exemples d&rsquo;utilisation de prompt chaining<\/h3>\n<ul>\n<li><strong>Analyse de donn\u00e9es\u00a0:<\/strong> Extraire des donn\u00e9es, les nettoyer, les transformer, puis les visualiser.<\/li>\n<li><strong>Cr\u00e9ation de contenu\u00a0:<\/strong> G\u00e9n\u00e9rer un plan, r\u00e9diger des paragraphes, puis les assembler en un article complet.<\/li>\n<li><strong>Traduction et r\u00e9sum\u00e9\u00a0:<\/strong> Traduire un texte, puis le r\u00e9sumer dans la langue cible.<\/li>\n<\/ul>\n<h3>Termes associ\u00e9s<\/h3>\n<ul id=\"TermesAssocies\">\n<li><a href=\"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/?s=Prompt+engineering\">Prompt engineering<\/a><\/li>\n<li><a href=\"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/?s=Few-shot+learning\">Few-shot learning<\/a><\/li>\n<li><a href=\"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/?s=Large+language+models+%28LLMs%29\">Large language models (LLMs)<\/a><\/li>\n<li><a href=\"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/?s=Cha%C3%AEnage+de+t%C3%A2ches\">Cha\u00eenage de t\u00e2ches<\/a><\/li>\n<li><a href=\"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/?s=Raisonnement+%C3%A9tape+par+%C3%A9tape\">Raisonnement \u00e9tape par \u00e9tape<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Le prompt chaining est une technique avanc\u00e9e en prompt engineering, un domaine crucial pour interagir efficacement avec les mod\u00e8les d&rsquo;intelligence artificielle. Qu&rsquo;est-ce que prompt chaining ? C&rsquo;est le fait d&rsquo;encha\u00eener plusieurs prompts pour d\u00e9composer une t\u00e2che complexe en sous-t\u00e2ches plus simples. Comment fonctionne prompt chaining ? Au lieu de soumettre un seul prompt complexe \u00e0 [&hellip;]<\/p>\n","protected":false},"author":0,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[71],"tags":[90,86,88,89,12,87],"class_list":["post-208","post","type-post","status-publish","format-standard","hentry","category-p","tag-chainage-de-taches","tag-few-shot-learning","tag-large-language-models-llms","tag-prompt-chaining","tag-prompt-engineering","tag-raisonnement-etape-par-etape"],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false},"uagb_author_info":{"display_name":"","author_link":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"Le prompt chaining est une technique avanc\u00e9e en prompt engineering, un domaine crucial pour interagir efficacement avec les mod\u00e8les d&rsquo;intelligence artificielle. Qu&rsquo;est-ce que prompt chaining ? C&rsquo;est le fait d&rsquo;encha\u00eener plusieurs prompts pour d\u00e9composer une t\u00e2che complexe en sous-t\u00e2ches plus simples. Comment fonctionne prompt chaining ? Au lieu de soumettre un seul prompt complexe \u00e0\u2026","_links":{"self":[{"href":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/wp-json\/wp\/v2\/posts\/208","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/wp-json\/wp\/v2\/comments?post=208"}],"version-history":[{"count":2,"href":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/wp-json\/wp\/v2\/posts\/208\/revisions"}],"predecessor-version":[{"id":609,"href":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/wp-json\/wp\/v2\/posts\/208\/revisions\/609"}],"wp:attachment":[{"href":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/wp-json\/wp\/v2\/media?parent=208"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/wp-json\/wp\/v2\/categories?post=208"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/wp-json\/wp\/v2\/tags?post=208"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}