{"id":3341,"date":"2025-09-24T20:59:48","date_gmt":"2025-09-24T12:59:48","guid":{"rendered":"https:\/\/moresourcing.com\/how-llms-work\/"},"modified":"2025-09-24T20:59:48","modified_gmt":"2025-09-24T12:59:48","slug":"how-llms-work","status":"publish","type":"post","link":"https:\/\/moresourcing.com\/fr\/how-llms-work\/","title":{"rendered":"Comment fonctionnent les LLM\u00a0: les 10 principales questions de niveau ex\u00e9cutif"},"content":{"rendered":"<p><\/p>\n<div>\n<div class=\"article-left-col\">\n<section class=\"article-topics\">\n<h4 class=\"article-topics__title\">Th\u00e8mes<\/h4>\n<ul class=\"article-topics__list\">\n<li class=\"article-topics__item\">\n                <a href=\"https:\/\/sloanreview.mit.edu\/topic\/data-ai-machine-learning\/\">Donn\u00e9es, IA et apprentissage automatique<\/a>\n            <\/li>\n<li class=\"article-topics__item\">\n                <a href=\"https:\/\/sloanreview.mit.edu\/topic\/ai-machine-learning\/\">IA et apprentissage automatique<\/a>\n            <\/li>\n<\/ul>\n<\/section>\n<section class=\"article-section\">\n<h4 class=\"article-section__title\">Colonne<\/h4>\n<p>\n            Nos chroniqueurs experts proposent des opinions et des analyses sur des questions importantes auxquelles sont confront\u00e9s les entreprises et les managers modernes.        <\/p>\n<p>        <a href=\"https:\/\/sloanreview.mit.edu\/series\/column\/\" class=\"article-section__link\"><\/p>\n<p>           Plus dans cette s\u00e9rie<br \/>\n                      <\/a><\/p>\n<\/section><\/div>\n<aside class=\"article-ad ad-300  ad-300x250 ad-desktop\">\n<\/aside>\n<aside class=\"article-ad ad-300  ad-300x250 ad-mobile\">\n<\/aside>\n<figure class=\"article-inline\">\n<img fetchpriority=\"high\" decoding=\"async\" width=\"1290\" height=\"860\" class=\"wp-image-122908\" srcset=\"https:\/\/moresourcing.com\/wp-content\/uploads\/2025\/09\/How-LLMs-Work-Top-10-Executive-Level-Questions.jpg 1290w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/09\/Ramakrishnan-Questions-1290x860-1-300x200.jpg 300w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/09\/Ramakrishnan-Questions-1290x860-1-150x100.jpg 150w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/09\/Ramakrishnan-Questions-1290x860-1-768x512.jpg 768w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/09\/Ramakrishnan-Questions-1290x860-1-764x509.jpg 764w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/09\/Ramakrishnan-Questions-1290x860-1-382x255.jpg 382w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/09\/Ramakrishnan-Questions-1290x860-1-870x580.jpg 870w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/09\/Ramakrishnan-Questions-1290x860-1-435x290.jpg 435w\" data-lazy-sizes=\"(max-width: 1290px) 100vw, 1290px\" src=\"https:\/\/moresourcing.com\/wp-content\/uploads\/2025\/09\/How-LLMs-Work-Top-10-Executive-Level-Questions.jpg\"\/><img fetchpriority=\"high\" decoding=\"async\" width=\"1290\" height=\"860\" src=\"https:\/\/moresourcing.com\/wp-content\/uploads\/2025\/09\/How-LLMs-Work-Top-10-Executive-Level-Questions.jpg\" class=\"wp-image-122908\" srcset=\"https:\/\/moresourcing.com\/wp-content\/uploads\/2025\/09\/How-LLMs-Work-Top-10-Executive-Level-Questions.jpg 1290w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/09\/Ramakrishnan-Questions-1290x860-1-300x200.jpg 300w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/09\/Ramakrishnan-Questions-1290x860-1-150x100.jpg 150w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/09\/Ramakrishnan-Questions-1290x860-1-768x512.jpg 768w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/09\/Ramakrishnan-Questions-1290x860-1-764x509.jpg 764w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/09\/Ramakrishnan-Questions-1290x860-1-382x255.jpg 382w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/09\/Ramakrishnan-Questions-1290x860-1-870x580.jpg 870w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/09\/Ramakrishnan-Questions-1290x860-1-435x290.jpg 435w\" sizes=\"(max-width: 1290px) 100vw, 1290px\"\/><figcaption>\n<p class=\"attribution\">Carolyn Geason-Beissel\/MIT SMR | Getty Images<\/p>\n<\/figcaption><\/figure>\n<div class=\"article-summary\"><strong class=\"article-summary__strong\">R\u00e9sum\u00e9 : <\/strong><\/p>\n<p>Les chefs d\u2019entreprise repensent d\u00e9sormais les flux de travail, la conception organisationnelle et d\u2019autres disciplines \u00e0 mesure que leurs entreprises adoptent l\u2019intelligence artificielle et les outils d\u2019IA g\u00e9n\u00e9rative.<\/p>\n<\/div>\n<p><span class=\"smr-leadin\">Dans mon travail<\/span> \u00c0 la MIT Sloan School of Management, j'ai enseign\u00e9 les bases du fonctionnement des grands mod\u00e8les linguistiques (LLM) \u00e0 de nombreux cadres au cours des deux derni\u00e8res ann\u00e9es. <\/p>\n<p>Certaines personnes postulent que les chefs d\u2019entreprise ne veulent ni n\u2019ont besoin de savoir comment fonctionnent les LLM et les outils d\u2019IA g\u00e9n\u00e9rative qu\u2019ils alimentent \u2013 \u200b\u200bet ne s\u2019int\u00e9ressent qu\u2019au <em>r\u00e9sultats<\/em> les outils peuvent fournir. <\/p>\n<p>Dans cette chronique, je partage des questions sur 10 sujets souvent mal compris sur lesquels on me pose souvent des questions, ainsi que leurs r\u00e9ponses.<\/p>\n<h3>10 questions et r\u00e9ponses essentielles sur GenAI et les LLM<\/h3>\n<h4>1. Je comprends que les LLM g\u00e9n\u00e8rent une sortie de texte \u00e0 la fois.<\/h4>\n<p>En d\u2019autres termes, quand le LLM d\u00e9cide-t-il de donner \u00e0 l\u2019utilisateur la r\u00e9ponse finale \u00e0 une question ?<\/p>\n<p>Lorsqu'un LLM r\u00e9pond \u00e0 une question, il produit du texte petit morceau \u00e0 la fois. <em>jeton<\/em>.<a id=\"reflink1\" class=\"reflink\" href=\"#ref1\">1<\/a> Les jetons peuvent \u00eatre des mots ou des parties de mots.<em> et <\/em>ce qu'il a d\u00e9j\u00e0 \u00e9crit jusqu'\u00e0 pr\u00e9sent.<a id=\"reflink2\" class=\"reflink\" href=\"#ref2\">2<\/a><\/p>\n<p>Un syst\u00e8me externe ex\u00e9cute le LLM dans une boucle \u00ab g\u00e9n\u00e9rer le jeton suivant ; l'ajouter \u00e0 l'entr\u00e9e ; g\u00e9n\u00e9rer le jeton suivant \u00bb jusqu'\u00e0 ce qu'un <em>condition d'arr\u00eat<\/em> est d\u00e9clench\u00e9.<\/p>\n<p>De nombreuses conditions d\u2019arr\u00eat sont utilis\u00e9es en pratique. <em>s\u00e9quence d'arr\u00eat<\/em>.<\/p>\n<p>Lorsque nous utilisons la version Web d'un outil comme ChatGPT en tant que consommateurs, nous ne voyons pas ce processus, seulement le texte final.<\/p>\n<p>Le point important ici est que la \u00ab d\u00e9cision \u00bb d\u2019arr\u00eater est une interaction entre les pr\u00e9dictions symboliques du LLM et la logique de contr\u00f4le externe, et non une d\u00e9cision prise par le LLM.<\/p>\n<h4>2. Si le LLM fait une erreur et que je la corrige, sera-t-il mis \u00e0 jour imm\u00e9diatement ?<\/h4>\n<p>Non, le LLM ne se mettra pas \u00e0 jour imm\u00e9diatement si vous le corrigez. <\/p>\n<p>Certaines applications, telles que ChatGPT, disposent d'une fonction de m\u00e9moire qui peut se mettre \u00e0 jour en temps r\u00e9el pour m\u00e9moriser des informations personnelles telles que votre nom, vos pr\u00e9f\u00e9rences ou votre emplacement.<\/p>\n<h4>3. Si le LLM g\u00e9n\u00e8re \u00e0 plusieurs reprises un jeton \u00e0 la fois en fonction de la conversation en cours, pourquoi l'ai-je vu utiliser les informations d'une conversation pr\u00e9c\u00e9dente (disons, d'il y a une semaine) dans la r\u00e9ponse\u00a0?<\/h4>\n<p>Les LLM g\u00e9n\u00e8rent des r\u00e9ponses un jeton \u00e0 la fois, en fonction des informations qui leur sont fournies dans cette conversation.<\/p>\n<p>Lorsque vous d\u00e9marrez une nouvelle conversation, des \u00e9l\u00e9ments pertinents de cette m\u00e9moire stock\u00e9e peuvent \u00eatre <em>automatiquement<\/em> ajout\u00e9 \u00e0 l'invite <em>dans les coulisses<\/em>.<\/p>\n<p>Les d\u00e9tails de ce qui est stock\u00e9 et du moment o\u00f9 il est utilis\u00e9 varient selon le fournisseur, et les m\u00e9thodes exactes n'ont pas \u00e9t\u00e9 divulgu\u00e9es.<\/p>\n<p>RAG, si vous n'\u00eates pas familier, est une technique utilis\u00e9e pour fournir au LLM l'acc\u00e8s \u00e0 un ensemble sp\u00e9cifique de donn\u00e9es propri\u00e9taires.<\/p>\n<h4>4. Je comprends que les LLM ont une date limite de formation et qu'ils ne \u00ab savent \u00bb pas ce qui s'est pass\u00e9 apr\u00e8s cette date. <em>peut<\/em> r\u00e9pondre aux questions sur les \u00e9v\u00e9nements qui se sont produits <em>apr\u00e8s<\/em> la date limite.<\/h4>\n<p>Lorsque vous posez une question sur quelque chose qui s'est produit apr\u00e8s la date limite de formation d'un LLM, le mod\u00e8le lui-m\u00eame ne \u00ab conna\u00eet \u00bb pas l'\u00e9v\u00e9nement \u00e0 moins qu'il n'ait acc\u00e8s \u00e0 des informations \u00e0 jour. <\/p>\n<div class=\"callout-pullquote callout-pullquote--no-quote callout-pullquote--long\" data-aos-duration=\"900\" data-aos-anchor-placement=\"bottom-bottom\" data-aos-easing=\"ease-out-back\" data-aos=\"fade-up\">\n<p class=\"callout-pullquote__quote\">\n\t\t\t\t\tSans acc\u00e8s aux donn\u00e9es en direct, un mod\u00e8le peut toujours g\u00e9n\u00e9rer une r\u00e9ponse bas\u00e9e sur ses donn\u00e9es d'entra\u00eenement qui ne refl\u00e8te pas les mises \u00e0 jour du monde r\u00e9el.\n\t\t\t\t\t<\/p>\n<\/div>\n<p>Dans ces cas, le LLM peut g\u00e9n\u00e9rer une requ\u00eate de recherche bas\u00e9e sur votre question, et une partie distincte du syst\u00e8me (en dehors du mod\u00e8le lui-m\u00eame) effectue la recherche.<\/p>\n<aside class=\"article-ad ad-300  ad-300x600 ad-desktop\">\n<\/aside>\n<aside class=\"article-ad ad-300  ad-300x250 ad-mobile\">\n<\/aside>\n<h4>5. Si j'inclus des documents dans le cadre d'une invite, puis-je m'assurer que le LLM utilise uniquement les documents fournis lorsqu'il g\u00e9n\u00e8re la r\u00e9ponse\u00a0?<\/h4>\n<p>Non. Bien que des invites minutieuses et des techniques telles que RAG puissent encourager un mod\u00e8le d'IA \u00e0 donner la priorit\u00e9 \u00e0 un ensemble de documents fournis, les LLM standard ne peuvent pas \u00eatre forc\u00e9s \u00e0 utiliser uniquement ce contenu. <\/p>\n<h4>6. Les LLM citent parfois les sources qui ont \u00e9t\u00e9 utilis\u00e9es pour g\u00e9n\u00e9rer la r\u00e9ponse \u00e0 une question.<\/h4>\n<p>Les LLM peuvent fabriquer (halluciner) des citations ou utiliser des sources r\u00e9elles de mani\u00e8re inexacte ou trompeuse.<\/p>\n<h4>7. Lorsque nous avons de nombreux documents, nous utilisons RAG, o\u00f9 nous collectons d'abord les informations pertinentes \u00e0 partir des documents et incluons uniquement celles-ci dans l'invite. <em>tous<\/em> les documents.<\/h4>\n<p>Les LLM modernes comme GPT-4.1 et Gemini 2.5 offrent des fen\u00eatres contextuelles contenant des millions de jetons, suffisamment pour contenir des livres entiers.<\/p>\n<p>Bien que ces fen\u00eatres contextuelles \u00e9tendues soient puissantes, inclure tous les documents dans l\u2019invite n\u2019est pas toujours une bonne id\u00e9e.<\/p>\n<p>Premi\u00e8rement, RAG ne consiste pas seulement \u00e0 garder une invite courte. <a href=\"https:\/\/www.dbreunig.com\/2025\/06\/22\/how-contexts-fail-and-how-to-fix-them.html\" target=\"_blank\" rel=\"noopener\">Surcharger le contexte<\/a> avec trop d'informations ou des informations non pertinentes peut nuire aux performances, et garder le contexte et l'invite pertinents, concis et pr\u00e9cis conduit souvent \u00e0 de meilleures r\u00e9ponses.<\/p>\n<p>Deuxi\u00e8mement, m\u00eame si les LLM peuvent accepter des contextes longs, ils ne traitent pas toutes les parties de la m\u00eame mani\u00e8re.<\/p>\n<p>Enfin, des invites plus longues signifient plus de jetons, ce qui augmente les co\u00fbts des API et ralentit les r\u00e9ponses.<\/p>\n<p>En bref, les longues fen\u00eatres contextuelles sont utiles, mais elles ne rendent pas la r\u00e9cup\u00e9ration obsol\u00e8te. <a href=\"https:\/\/sloanreview.mit.edu\/article\/the-genai-app-step-youre-skimping-on-evaluations\/\">\u00e9valu\u00e9<\/a> en fonction des besoins de votre application sp\u00e9cifique.<\/p>\n<h4>8. Les hallucinations LLM peuvent-elles \u00eatre \u00e9limin\u00e9es ?<\/h4>\n<p>Non, les hallucinations ne peuvent pas \u00eatre compl\u00e8tement \u00e9limin\u00e9es avec la technologie LLM actuelle.<\/p>\n<p>Cependant, une ing\u00e9nierie minutieuse des invites et des strat\u00e9gies telles que RAG, un r\u00e9glage fin des donn\u00e9es sp\u00e9cifiques au domaine et un post-traitement avec des contr\u00f4les bas\u00e9s sur des r\u00e8gles ou une validation externe peuvent <a href=\"https:\/\/docs.anthropic.com\/en\/docs\/test-and-evaluate\/strengthen-guardrails\/reduce-hallucinations\" target=\"_blank\">r\u00e9duire les hallucinations<\/a> dans des cas d'utilisation sp\u00e9cifiques.<a id=\"reflink3\" class=\"reflink\" href=\"#ref3\">3<\/a> Bien que ces strat\u00e9gies ne garantissent pas l\u2019\u00e9limination des hallucinations, elles peuvent am\u00e9liorer suffisamment la fiabilit\u00e9 d\u2019un LLM pour de nombreuses applications pratiques.<\/p>\n<h4>9. Puisque les hallucinations et les erreurs du LLM ne peuvent pas \u00eatre \u00e9limin\u00e9es, nous devons v\u00e9rifier les r\u00e9ponses.<\/h4>\n<p>La v\u00e9rification efficace des r\u00e9sultats du LLM d\u00e9pend du type de t\u00e2che et du niveau de risque acceptable.<\/p>\n<p>Pour les t\u00e2ches ouvertes telles que les r\u00e9sum\u00e9s, les essais, les rapports ou les analyses, l\u2019examen humain offre la surveillance la plus fiable.<\/p>\n<p>Une alternative de plus en plus populaire consiste \u00e0 utiliser un \u00ab juge IA \u00bb, qui est g\u00e9n\u00e9ralement un autre LLM capable d\u2019\u00e9valuer ou de v\u00e9rifier les r\u00e9sultats du premier outil. <\/p>\n<div class=\"callout-pullquote callout-pullquote--no-quote\" data-aos-duration=\"900\" data-aos-anchor-placement=\"bottom-bottom\" data-aos-easing=\"ease-out-back\" data-aos=\"fade-new-left\">\n<p class=\"callout-pullquote__quote\">\n\t\t\t\t\tUn \u00ab juge IA \u00bb est g\u00e9n\u00e9ralement un autre LLM utilis\u00e9 pour \u00e9valuer ou v\u00e9rifier les r\u00e9sultats du premier outil.\n\t\t\t\t\t<\/p>\n<\/div>\n<p>Les t\u00e2ches structur\u00e9es, telles que la g\u00e9n\u00e9ration de code, la classification d'informations ou la production de donn\u00e9es structur\u00e9es dans des formats tels que SQL ou JSON, se pr\u00eatent plus facilement \u00e0 l'automatisation. <\/p>\n<p>En r\u00e9sum\u00e9, les strat\u00e9gies de contr\u00f4le les plus efficaces combinent automatisation et surveillance humaine.<\/p>\n<h4>10. Nous construisons un chatbot bas\u00e9 sur LLM et souhaitons garantir que sa r\u00e9ponse \u00e0 une question reste inchang\u00e9e lorsque diff\u00e9rents utilisateurs posent la m\u00eame question (ou qu'un utilisateur pose la m\u00eame question \u00e0 des moments diff\u00e9rents).<\/h4>\n<p>Si par \u00ab garantie \u00bb vous entendez <em>exactement<\/em> toujours la m\u00eame formulation, la r\u00e9ponse courte est non.<\/p>\n<p>Si la m\u00eame question est pos\u00e9e \u00e0 diff\u00e9rentes occasions en utilisant des mots diff\u00e9rents, les r\u00e9ponses du LLM changeront tr\u00e8s probablement. <em>exactement<\/em> la m\u00eame r\u00e9ponse sera g\u00e9n\u00e9r\u00e9e \u00e0 chaque fois.<\/p>\n<p>Vous pouvez r\u00e9duire la variabilit\u00e9 en configurant certains param\u00e8tres LLM (par exemple, en r\u00e9glant la \u00ab temp\u00e9rature \u00bb \u00e0 z\u00e9ro), en verrouillant la version exacte du mod\u00e8le et m\u00eame en auto-h\u00e9bergant afin de contr\u00f4ler l'ensemble de la pile mat\u00e9rielle et logicielle.<a id=\"reflink4\" class=\"reflink\" href=\"#ref4\">4<\/a> Ainsi, vous verrez encore occasionnellement de petits changements de formulation ou d\u2019accentuation qui ne changent pas le sens de la r\u00e9ponse sous-jacente. <\/p>\n<p>La seule fa\u00e7on de garantir v\u00e9ritablement une formulation identique est de stocker (mettre en cache) la r\u00e9ponse la premi\u00e8re fois qu'elle est g\u00e9n\u00e9r\u00e9e et de servir ce texte stock\u00e9 chaque fois que la m\u00eame question est d\u00e9tect\u00e9e.<\/p>\n<p>En bref : vous pouvez donner des r\u00e9ponses extr\u00eamement coh\u00e9rentes, mais une garantie de formulation \u00e0 100 % n'est pas r\u00e9alisable avec la technologie actuelle.<\/p>\n<aside class=\"article-ad ad-300  ad-300x250 ad-desktop\">\n<\/aside>\n<aside class=\"article-ad ad-300  ad-300x250 ad-mobile\">\n<\/aside>\n<div class=\"article-authors\" id=\"article-authors\">\n<h4 class=\"article-authors__title\">A propos de l'auteur<\/h4>\n<div class=\"article-authors__bio\">\n<p>Rama Ramakrishnan est professeur de pratique \u00e0 la MIT Sloan School of Management.<\/p>\n<\/div><\/div>\n<div class=\"article-ref\" id=\"article-ref\">\n<h4 class=\"article-ref__title\">R\u00e9f\u00e9rences<\/h4>\n<div class=\"article-ref__list\">\n<p id=\"ref1\"><b>1.<\/b> En moyenne, un jeton repr\u00e9sente environ les trois quarts d'un mot, et les LLM modernes ont un vocabulaire allant de dizaines de milliers \u00e0 plus de 100 000 jetons. <a href=\"https:\/\/platform.openai.com\/tokenizer\" target=\"_blank\">L'outil Tokenizer d'OpenAI<\/a> et voyez comment un mot est symbolis\u00e9 pour acqu\u00e9rir une compr\u00e9hension plus profonde.<\/p>\n<p id=\"ref2\"><b>2.<\/b> \u00c0 proprement parler, \u00e9tant donn\u00e9 une entr\u00e9e, le LLM g\u00e9n\u00e8re une probabilit\u00e9 (c'est-\u00e0-dire un nombre compris entre 0,0 et 1,0) pour chaque jeton de son vocabulaire.<\/p>\n<p id=\"ref3\"><b>3.<\/b> Pour une \u00e9tude r\u00e9cente des recherches universitaires sur ce sujet, voir Y. Wang, M. Wang, M.A. Manzoor et al., \u00ab<a href=\"https:\/\/aclanthology.org\/2024.emnlp-main.1088.pdf\" target=\"_blank\">Actualit\u00e9 des grands mod\u00e8les de langage\u00a0: une enqu\u00eate<\/a>\u00bb, dans \u00ab Actes de la conf\u00e9rence 2024 sur les m\u00e9thodes empiriques dans le traitement du langage naturel \u00bb (Miami : Association for Computational Linguistics, 12-16 novembre 2024), 19519-19529.<\/p>\n<p id=\"ref4\"><b>4.<\/b> Pour n'en nommer que quelques-uns\u00a0: op\u00e9rations GPU non d\u00e9terministes, diff\u00e9rences d'arrondi en virgule flottante et mises \u00e0 jour silencieuses du back-end.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\n<p>#LLM #Travail #Top #ExecutiveLevel #Questions<\/p>","protected":false},"excerpt":{"rendered":"<p>Sujets Donn\u00e9es, IA et apprentissage automatique Chronique IA et apprentissage automatique Nos chroniqueurs experts offrent des opinions et des analyses sur les probl\u00e8mes importants auxquels sont confront\u00e9s les entreprises et les gestionnaires modernes.<\/p>","protected":false},"author":1,"featured_media":3342,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"footnotes":""},"categories":[9],"tags":[],"class_list":["post-3341","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-management"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.7.1 (Yoast SEO v25.8) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How LLMs Work: Top 10 Executive-Level Questions - MORE SOURCING LTD<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/moresourcing.com\/fr\/how-llms-work\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How LLMs Work: Top 10 Executive-Level Questions\" \/>\n<meta property=\"og:description\" content=\"Topics Data, AI, &amp; Machine Learning AI &amp; Machine Learning Column Our expert columnists offer opinion and analysis on important issues facing modern businesses and managers. 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