{"id":7825,"date":"2024-05-14T11:18:00","date_gmt":"2024-05-14T09:18:00","guid":{"rendered":"http:\/\/nextbrain.ai\/?p=7825"},"modified":"2024-05-14T11:22:02","modified_gmt":"2024-05-14T09:22:02","slug":"rag-explained-in-simple-words","status":"publish","type":"post","link":"https:\/\/nextbrain.ai\/es\/blog\/rag-explained-in-simple-words","title":{"rendered":"RAG explicado en palabras simples"},"content":{"rendered":"
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La Generaci\u00f3n Aumentada por Recuperaci\u00f3n (RAG) es el m\u00e9todo de perfeccionar la salida de un modelo de lenguaje sustancial al referirse a una base de conocimiento cre\u00edble m\u00e1s all\u00e1 de sus fuentes de datos de entrenamiento antes de generar una respuesta.<\/p>

En este art\u00edculo, explicaremos c\u00f3mo funciona realmente en palabras simples.<\/p>

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Imagina que tenemos un usuario. El usuario podr\u00eda ser una persona, un bot o otra aplicaci\u00f3n, todos buscando respuestas a preguntas espec\u00edficas. Por ejemplo, podr\u00edan preguntar: \"\u00bfCu\u00e1l fue la tasa de cancelaci\u00f3n en el primer trimestre para los clientes de la regi\u00f3n sur?\"<\/p>

La parte inicial de esta pregunta suele estar dentro del alcance de un modelo de lenguaje grande (LLM) general. Sin embargo, la especificidad de los datos requeridos para \"Q1 de los clientes del sur\" no est\u00e1 disponible directamente en los LLM, ya que es \u00fanica para el negocio y cambia con el tiempo.<\/p>

Para gestionar esto, podr\u00edan ser necesarias m\u00faltiples fuentes de datos. Estas podr\u00edan incluir PDFs, otras aplicaciones comerciales o incluso im\u00e1genes. Acceder a los datos correctos es crucial para responder a consultas tan espec\u00edficas con precisi\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

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El papel de las bases de datos vectoriales en RAG<\/strong><\/h3>

Una vez que re\u00fanas los datos necesarios, se ingresan en una base de datos vectorial<\/span>Esta base de datos organiza tanto datos estructurados como no estructurados en un formato de matriz matem\u00e1tica, que es m\u00e1s comprensible para los modelos de aprendizaje autom\u00e1tico e inteligencia artificial generativa en comparaci\u00f3n con los datos sin procesar. Al consultar esta base de datos vectorial, obtienes una incrustaci\u00f3n que contiene datos relevantes para tu pregunta.<\/p>

Esta incrustaci\u00f3n se reintroduce luego en el LLM, enriqueciendo el aviso original con datos precisos y con fuentes. El LLM procesa este aviso mejorado y entrega la respuesta a la pregunta original, asegurando precisi\u00f3n y relevancia.<\/p>

A medida que nuevos datos ingresan a la base de datos vectorial, se actualizan las incrustaciones relevantes para consultas en curso, como la tasa de abandono en el primer trimestre. Esta actualizaci\u00f3n continua garantiza que las consultas posteriores reciban la informaci\u00f3n m\u00e1s actual y relevante.<\/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

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Mitigaci\u00f3n de riesgos en el an\u00e1lisis de datos impulsado por IA<\/strong><\/h3>\n

La calidad de los datos que ingresan a la base de datos vectorial es muy importante para los resultados producidos. Asegurar datos limpios, bien gobernados y correctamente gestionados es esencial. Adem\u00e1s, la transparencia de los LLM utilizados en el proceso tambi\u00e9n es crucial. Es importante utilizar LLM que sean transparentes en sus procesos de entrenamiento para garantizar la fiabilidad y precisi\u00f3n.<\/p>\n

En 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. Programa tu demostraci\u00f3n hoy.<\/a> experimentar 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

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\"Reservar\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":"

Retrieval-Augmented Generation (RAG) is the method of refining the output of a substantial language model by referring to a credible knowledge base beyond its training […]<\/p>\n","protected":false},"author":1,"featured_media":7826,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[70],"tags":[598,601,290,434,264,597,600,282,587,599],"class_list":["post-7825","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-ai-driven-data-analysis","tag-business-insights","tag-data-analysis-2","tag-data-governance","tag-generative-ai","tag-large-language-model","tag-llm","tag-machine-learning","tag-rag","tag-vector-database"],"yoast_head":"\nRAG Explained in Simple Words - NextBrain AI | No-Code Machine Learning<\/title>\n<meta name=\"description\" content=\"Discover how Retrieval-Augmented Generation (RAG) revolutionizes data handling with vector databases and large language models.\" \/>\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\/rag-explained-in-simple-words\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"RAG Explained in Simple Words - 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