{"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\/es\/blog\/fine-tuning-or-rag-whats-the-best-approach","title":{"rendered":"Ajuste fino o RAG: \u00bfCu\u00e1l es el mejor enfoque?"},"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\">Supongamos que necesitas construir un chatbot de atenci\u00f3n al cliente con inteligencia artificial. Incluso si tu modelo est\u00e1 ajustado con un conjunto de datos de entrenamiento espec\u00edfico, ser\u00eda ineficaz sin acceso a datos como conversaciones pasadas o informaci\u00f3n de productos almacenada en los CRM, documentos o sistemas de tickets de los clientes.<\/p><p class=\"p1\">Para utilizar estos datos contextuales, necesitas integrarlos con tus LLMs. Esto implica la ingesta de datos de fuentes de terceros y elegir entre RAG y ajuste fino para utilizar los datos de manera efectiva.<\/p><p>Pero, \u00bfcu\u00e1l es el mejor enfoque: el ajuste fino o la Generaci\u00f3n Aumentada por Recuperaci\u00f3n (RAG)? Este art\u00edculo ofrece una comparaci\u00f3n detallada de ambos.<\/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>Generaci\u00f3n Aumentada por Recuperaci\u00f3n (RAG)<\/h3><p>RAG mejora la precisi\u00f3n de los LLMs al recuperar datos externos bajo demanda e inyectar contexto en los mensajes en tiempo de ejecuci\u00f3n. Estos datos pueden provenir de diversas fuentes como documentaci\u00f3n de clientes, p\u00e1ginas web y aplicaciones de terceros como CRMs y Google Drive.<\/p><h4>Componentes Clave de RAG<\/h4><ol><li><p><strong>Ingesta y Almacenamiento de Datos<\/strong>:<\/p><ul><li><strong>: Extraer todos los datos relevantes del cliente inicialmente.<\/strong>: Extraer todos los datos relevantes del cliente inicialmente.<\/li><li><strong>: Utilizar trabajos en segundo plano para mantener los datos actualizados en tiempo real.<\/strong>Utiliza trabajos en segundo plano para mantener los datos actualizados en tiempo real.<\/li><li><strong>Embeddings y Almacenamiento<\/strong>: Almacenar los datos en una base de datos vectorial para su recuperaci\u00f3n.<\/li><\/ul><\/li><li><p><strong>Inyecci\u00f3n de Prompt<\/strong>:<\/p><ul><li><strong>En Tiempo de Ejecuci\u00f3n<\/strong>: Recuperar fragmentos de texto relevantes de la base de datos vectorial e inyectarlos en el prompt\/query inicial para que el LLM genere la respuesta final.<\/li><\/ul><\/li><\/ol><h3>Ajuste Fino<\/h3><p>El ajuste fino implica un entrenamiento adicional de un LLM preentrenado en un conjunto de datos espec\u00edfico del dominio para mejorar su rendimiento en tareas espec\u00edficas. Por ejemplo, ajustar un modelo en correos electr\u00f3nicos de ventas para construir un agente de ventas de IA.<\/p><h4>Desaf\u00edos del Ajuste Fino<\/h4><ul><li><strong>Preparaci\u00f3n de Datos<\/strong>: Requiere un conjunto de datos de entrenamiento limpio y bien estructurado.<\/li><li><strong>Resultados Predecibles<\/strong>: Produce resultados m\u00e1s predecibles, pero consume tiempo.<\/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. Ajuste Fino: \u00bfCu\u00e1l Elegir?<\/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>Cu\u00e1ndo Usar RAG<\/h3><ul><li>Inyecta contexto en tiempo real en las indicaciones.<\/li><li>No requiere un conjunto de datos estructurado para el entrenamiento.<\/li><li>Recupera contexto relevante de m\u00faltiples fuentes de datos.<\/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>Cu\u00e1ndo Usar Ajuste Fino<\/h3><ul><li>Cuando tienes un conjunto de datos espec\u00edfico y bien preparado para el entrenamiento.<\/li><li>Para tareas que requieren resultados predecibles.<\/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>Implementando 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>Ingesta de Datos<\/h3><p>Identifica d\u00f3nde reside tu datos contextuales, como en Notion, Google Drive, Slack, Salesforce, etc. Crea mecanismos para ingerir tanto los datos existentes como las actualizaciones.<\/p><h3>Divisi\u00f3n de Datos y Embebido<\/h3><p>La mayor\u00eda de los datos contextuales son no estructurados. Utiliza estrategias de divisi\u00f3n y genera embebidos para vectorizar los datos para b\u00fasquedas de similitud.<\/p><h3>Almacenamiento y Recuperaci\u00f3n de Datos<\/h3><p>Almacena los embebidos en una base de datos vectorial para una recuperaci\u00f3n r\u00e1pida. En tiempo de ejecuci\u00f3n, realiza b\u00fasquedas de similitud para recuperar fragmentos de datos relevantes e incl\u00fayelos en los mensajes.<\/p><h3>Seguridad y Permisos<\/h3><p>Asegura un almacenamiento seguro y permisos adecuados para prevenir filtraciones de datos. Considera utilizar LLMs de nivel empresarial o desplegar instancias separadas para cada cliente para mejorar la seguridad.<\/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>Proceso de Ajuste Fino<\/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=\"Ajuste fino\" alt=\"Ajuste fino\" 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>Ingesta de Datos y Preparaci\u00f3n<\/h3><p>Ingestar datos de aplicaciones externas y preparar conjuntos de datos de entrenamiento limpios. Validar estos conjuntos de datos para asegurar entradas de calidad.<\/p><h3>Entrenamiento y Validaci\u00f3n<\/h3><p>Ajustar finamente el modelo con los conjuntos de datos preparados. Validar el modelo para asegurar que cumple con los criterios de rendimiento antes del despliegue.<\/p><h3>Aprendizaje por Refuerzo<\/h3><p>Implementar bucles de aprendizaje por refuerzo en producci\u00f3n para mejorar continuamente el modelo utilizando la retroalimentaci\u00f3n del usuario.<\/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>Tanto RAG como el ajuste fino son valiosos para integrar datos externos y mejorar las salidas de los LLM. Dadas las complejidades de construir conjuntos de datos de entrenamiento robustos, comenzar con RAG suele ser m\u00e1s beneficioso. Sin embargo, en muchos casos, combinar ambos enfoques puede volverse esencial.<\/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>En\u00a0<a href=\"http:\/\/nextbrain.ai\/es\/\">NextBrain IA<\/a>, utilizamos la \u00faltima tecnolog\u00eda de IA para ofrecer an\u00e1lisis de datos precisos y conocimientos empresariales accionables, sin las complejidades a menudo asociadas con las implementaciones t\u00e9cnicas.\u00a0<a href=\"http:\/\/nextbrain.ai\/es\/schedule-your-free-demo\/\">Programa tu demostraci\u00f3n hoy.<\/a>\u00a0experimentar de primera mano c\u00f3mo funciona nuestra soluci\u00f3n.<\/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\/es\/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=\"Reservar una demostraci\u00f3n\" alt=\"Reservar una demostraci\u00f3n\" 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\/es\/blog\/fine-tuning-or-rag-whats-the-best-approach\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Fine-tuning or RAG: What\u2019s the Best Approach - NextBrain AI | No-Code Machine Learning\" \/>\n<meta property=\"og:description\" content=\"Explore the differences between Retrieval Augmented Generation (RAG) and fine-tuning to optimize LLMs by integrating contextual data effectively.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/nextbrain.ai\/es\/blog\/fine-tuning-or-rag-whats-the-best-approach\" \/>\n<meta property=\"og:site_name\" content=\"NextBrain AI | No-Code Machine Learning\" \/>\n<meta property=\"article:published_time\" content=\"2024-07-19T08:14:11+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-07-19T08:14:14+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/nextbrain.ai\/wp-content\/uploads\/2024\/05\/RAG-or-Fine-tuning.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1080\" \/>\n\t<meta property=\"og:image:height\" content=\"1080\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"admin1061\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@nextbrain_ai\" \/>\n<meta name=\"twitter:site\" content=\"@nextbrain_ai\" \/>\n<meta name=\"twitter:label1\" content=\"Escrito por\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin1061\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Ajuste fino o RAG: \u00bfCu\u00e1l es el mejor enfoque? 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