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10:["$","p","p-2",{"className":"mb-4 text-gray-600 leading-relaxed","children":["$","strong","strong-0",{"children":"Architecture :"}]}]
11:["$","pre","pre-0",{"children":["$","code","code-0",{"className":"block bg-gray-900 text-gray-100 p-4 rounded-lg overflow-x-auto my-4 font-mono text-sm shadow-lg","children":"User Query → Embedding → Search in Vector DB → Retrieve Top-K Docs → Inject in Prompt → LLM → Response\n"}]}]
12:["$","p","p-3",{"className":"mb-4 text-gray-600 leading-relaxed","children":[["$","strong","strong-0",{"children":"Avantages :"}],"\n✅ Pas besoin de réentraîner le modèle\n✅ Les données sont toujours à jour (ajout/suppression facile)\n✅ Coût faible (juste le coût des embeddings et de la recherche)\n✅ Transparence : vous savez d'où viennent les réponses (citations)"]}]
13:["$","p","p-4",{"className":"mb-4 text-gray-600 leading-relaxed","children":[["$","strong","strong-0",{"children":"Inconvénients :"}],"\n❌ Limité par la fenêtre de contexte du LLM\n❌ Qualité dépendante de la pertinence du retrieval\n❌ Latence plus élevée (recherche + génération)"]}]
14:["$","p","p-5",{"className":"mb-4 text-gray-600 leading-relaxed","children":["$","strong","strong-0",{"children":"Cas d'usage :"}]}]
15:["$","ul","ul-0",{"className":"list-disc pl-6 mb-4 space-y-2","children":["\n",["$","li","li-0",{"className":"text-gray-600","children":[["$","strong","strong-0",{"children":"FAQ interne"}],", documentation technique"]}],"\n",["$","li","li-1",{"className":"text-gray-600","children":[["$","strong","strong-0",{"children":"Support client"}]," basé sur une base de connaissances"]}],"\n",["$","li","li-2",{"className":"text-gray-600","children":[["$","strong","strong-0",{"children":"Recherche dans des archives"}]," (juridique, compliance)"]}],"\n",["$","li","li-3",{"className":"text-gray-600","children":["$","strong","strong-0",{"children":"Tout use case où les données changent fréquemment"}]}],"\n"]}]
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17:["$","h2","h2-1",{"className":"text-3xl font-semibold mb-4 mt-12 text-gray-800 border-b pb-2","children":"Fine-tuning : Réentraînement du modèle"}]
18:["$","p","p-6",{"className":"mb-4 text-gray-600 leading-relaxed","children":[["$","strong","strong-0",{"children":"Principe :"}],"\nOn réentraîne (partiellement) le LLM sur vos données pour qu'il \"apprenne\" votre domaine, votre ton, vos patterns."]}]
19:["$","p","p-7",{"className":"mb-4 text-gray-600 leading-relaxed","children":["$","strong","strong-0",{"children":"Méthodes :"}]}]
1a:["$","ul","ul-1",{"className":"list-disc pl-6 mb-4 space-y-2","children":["\n",["$","li","li-0",{"className":"text-gray-600","children":[["$","strong","strong-0",{"children":"Full fine-tuning :"}]," Réentraîner tous les paramètres (coûteux, rare)"]}],"\n",["$","li","li-1",{"className":"text-gray-600","children":[["$","strong","strong-0",{"children":"LoRA (Low-Rank Adaptation) :"}]," Réentraîner seulement une petite partie (populaire, efficace)"]}],"\n"]}]
1b:["$","p","p-8",{"className":"mb-4 text-gray-600 leading-relaxed","children":[["$","strong","strong-0",{"children":"Avantages :"}],"\n✅ Le modèle \"comprend\" vraiment votre domaine\n✅ Meilleure performance sur des tâches spécifiques\n✅ Peut apprendre un ton, un style, des formats de sortie\n✅ Pas de limite de contexte pour les connaissances apprises"]}]
1c:["$","p","p-9",{"className":"mb-4 text-gray-600 leading-relaxed","children":[["$","strong","strong-0",{"children":"Inconvénients :"}],"\n❌ Coût élevé (données, compute, temps)\n❌ Nécessite un dataset de qualité (milliers d'exemples)\n❌ Mise à jour complexe (il faut réentraîner)\n❌ Risque d'overfitting et de catastrophic forgetting"]}]
1d:["$","p","p-10",{"className":"mb-4 text-gray-600 leading-relaxed","children":["$","strong","strong-0",{"children":"Cas d'usage :"}]}]
1e:["$","ul","ul-2",{"className":"list-disc pl-6 mb-4 space-y-2","children":["\n",["$","li","li-0",{"className":"text-gray-600","children":[["$","strong","strong-0",{"children":"Génération de contenu"}]," dans un style très spécifique"]}],"\n",["$","li","li-1",{"className":"text-gray-600","children":[["$","strong","strong-0",{"children":"Traduction"}]," dans un jargon métier"]}],"\n",["$","li","li-2",{"className":"text-gray-600","children":[["$","strong","strong-0",{"children":"Classification"}]," ou ",["$","strong","strong-1",{"children":"extraction d'entités"}]," sur un domaine très technique"]}],"\n",["$","li","li-3",{"className":"text-gray-600","children":[["$","strong","strong-0",{"children":"Tâches répétitives"}]," où la performance doit être maximale"]}],"\n"]}]
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23:["$","h2","h2-3",{"className":"text-3xl font-semibold mb-4 mt-12 text-gray-800 border-b pb-2","children":"Peut-on combiner les deux ?"}]
24:["$","p","p-11",{"className":"mb-4 text-gray-600 leading-relaxed","children":[["$","strong","strong-0",{"children":"Oui !"}]," C'est même souvent la meilleure approche :"]}]
25:["$","ol","ol-0",{"className":"list-decimal pl-6 mb-4 space-y-2","children":["\n",["$","li","li-0",{"className":"text-gray-600","children":[["$","strong","strong-0",{"children":"Fine-tuner"}]," le modèle pour apprendre le domaine, le ton, les formats"]}],"\n",["$","li","li-1",{"className":"text-gray-600","children":[["$","strong","strong-0",{"children":"Utiliser RAG"}]," pour injecter les données factuelles à jour"]}],"\n"]}]
26:["$","p","p-12",{"className":"mb-4 text-gray-600 leading-relaxed","children":[["$","strong","strong-0",{"children":"Exemple :"}],"\nVous fine-tunez un modèle sur le jargon médical, puis vous utilisez RAG pour récupérer les dernières publications scientifiques."]}]
27:["$","hr","hr-3",{}]
28:["$","h2","h2-4",{"className":"text-3xl font-semibold mb-4 mt-12 text-gray-800 border-b pb-2","children":"Comment décider ?"}]
29:["$","p","p-13",{"className":"mb-4 text-gray-600 leading-relaxed","children":["$","strong","strong-0",{"children":"Choisissez RAG si :"}]}]
2a:["$","ul","ul-3",{"className":"list-disc pl-6 mb-4 space-y-2","children":["\n",["$","li","li-0",{"className":"text-gray-600","children":"Vos données changent fréquemment"}],"\n",["$","li","li-1",{"className":"text-gray-600","children":"Vous avez besoin de transparence (citations)"}],"\n",["$","li","li-2",{"className":"text-gray-600","children":"Vous avez un budget limité"}],"\n",["$","li","li-3",{"className":"text-gray-600","children":"Vous voulez démarrer vite"}],"\n"]}]
2b:["$","p","p-14",{"className":"mb-4 text-gray-600 leading-relaxed","children":["$","strong","strong-0",{"children":"Choisissez Fine-tuning si :"}]}]
2c:["$","ul","ul-4",{"className":"list-disc pl-6 mb-4 space-y-2","children":["\n",["$","li","li-0",{"className":"text-gray-600","children":"Vous avez un dataset de qualité (milliers d'exemples)"}],"\n",["$","li","li-1",{"className":"text-gray-600","children":"Vous voulez une performance maximale sur une tâche précise"}],"\n",["$","li","li-2",{"className":"text-gray-600","children":"Vous pouvez investir du temps et de l'argent"}],"\n",["$","li","li-3",{"className":"text-gray-600","children":"Vos données sont stables"}],"\n"]}]
2d:["$","p","p-15",{"className":"mb-4 text-gray-600 leading-relaxed","children":["$","strong","strong-0",{"children":"Combinez les deux si :"}]}]
2e:["$","ul","ul-5",{"className":"list-disc pl-6 mb-4 space-y-2","children":["\n",["$","li","li-0",{"className":"text-gray-600","children":"Vous avez les ressources"}],"\n",["$","li","li-1",{"className":"text-gray-600","children":"Vous voulez le meilleur des deux mondes"}],"\n"]}]
2f:["$","hr","hr-4",{}]
30:["$","h2","h2-5",{"className":"text-3xl font-semibold mb-4 mt-12 text-gray-800 border-b pb-2","children":"Nos formations RAG et Fine-tuning"}]
31:["$","p","p-16",{"className":"mb-4 text-gray-600 leading-relaxed","children":"Chez Ikasia, nous proposons :"}]
32:["$","p","p-17",{"className":"mb-4 text-gray-600 leading-relaxed","children":[["$","strong","strong-0",{"children":"Atelier RAG Entreprise (3h30)"}],"\nConstruisez un système RAG sur SharePoint/Confluence avec évaluations et déploiement."]}]
33:["$","p","p-18",{"className":"mb-4 text-gray-600 leading-relaxed","children":[["$","strong","strong-0",{"children":"Bootcamp Data Science & ML (8 semaines)"}],"\nModule dédié au fine-tuning de LLMs avec LoRA et PEFT."]}]
34:["$","hr","hr-5",{}]
35:["$","h2","h2-6",{"className":"text-3xl font-semibold mb-4 mt-12 text-gray-800 border-b pb-2","children":"Conclusion"}]
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