{"id":10210,"date":"2024-10-21T11:32:05","date_gmt":"2024-10-21T16:32:05","guid":{"rendered":"https:\/\/activeone.co\/?p=10210"},"modified":"2025-07-03T22:23:54","modified_gmt":"2025-07-04T03:23:54","slug":"diversify-the-functionalities-of-your-tool-with-generative-artificial-intelligence-a-case-study-part-2","status":"publish","type":"post","link":"https:\/\/activeone.co\/es\/diversify-the-functionalities-of-your-tool-with-generative-artificial-intelligence-a-case-study-part-2\/","title":{"rendered":"Diversifica las funcionalidades de tu herramienta con Inteligencia Artificial Generativa, Un caso de estudio parte 2"},"content":{"rendered":"
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En semanas anteriores publicamos una experiencia de incorporaci\u00f3n de LLMs usando el modelo fundacional de ChatGPT para dar soluci\u00f3n a un problema generalizado de registro de tiempos<\/a><\/span>.<\/span><\/i> En esta nueva publicaci\u00f3n te contaremos como fue la fase 2 de este proyecto y c\u00f3mo solucionamos los problemas identificados.<\/div>\n\t\t\t\t
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Recordando los problemas<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tComo siempre hemos dicho, no todo es color de rosa\u2026 Anteriormente mencionamos que uno de los grandes problemas era que la IA no ten\u00eda la capacidad de abstraer correctamente la informaci\u00f3n de los clientes y los asuntos entregados en el prompt. Algunas veces no eran precisos y era dif\u00edcil hacer un match con la lista existente y otras veces ni siquiera lograba abstraerlo del contenido. \u00bfPero c\u00f3mo podr\u00edamos resolver esto?\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
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Resolviendo los retos<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Si lo pensamos detenidamente, todo lo que necesita la IA es conocer un poco m\u00e1s del dominio de nuestro negocio. Ahora la pregunta es \u00bfc\u00f3mo podemos entregarle a la IA esa informaci\u00f3n puntual, sin caer en sobrecostos enormes y en ineficiencias que nos lleven a obtener alucinaciones en el motor fundacional?<\/p>

La respuesta es: utilizando bases de datos vectoriales y el concepto de RAG (Retrieval Augmented Generate) para entregarle contexto a la IA.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Entendamos el concepto de RAG\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tRAG, o Recuperaci\u00f3n Aumentada con Generaci\u00f3n (Retrieve-Augment-Generate), no es m\u00e1s que una t\u00e9cnica que nos permite abstraer informaci\u00f3n relevante de documentos existentes combinando componentes de recuperaci\u00f3n, aumentado y generaci\u00f3n a las consultas realizadas. Ahondemos un poco m\u00e1s en cada fase:\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
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Recuperaci\u00f3n:<\/h4>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tEsta fase permite la abstracci\u00f3n de la informaci\u00f3n contenida en repositorios de informaci\u00f3n y la posterior segmentaci\u00f3n en bloques (chunks).\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
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Aumentado<\/h4>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tLa informaci\u00f3n previamente recuperada es procesada con el fin de extraer elementos y contextos espec\u00edficos que pueden enriquecer el prompt que se entrega al motor fundacional. Este proceso asegura que la informaci\u00f3n contenida en los bloques recuperados sea \u00fatil y precisa para la tarea entrante. Es en este proceso que se utilizan las bases de datos vectoriales que permiten el c\u00e1lculo de la distancia de los conceptos ligados al contexto.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
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Generaci\u00f3n<\/h4>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tFinalmente la informaci\u00f3n relevante es inyectada al prompt con el fin de darle contexto al modelo fundacional con el fin de generar respuestas y\/o soluciones m\u00e1s efectivas.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
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\u00bfY cu\u00e1les son las ventajas?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tUtilizando este tipo de t\u00e9cnicas podemos generar salidas m\u00e1s coherentes evitando alucinaciones y teniendo un contexto aterrizado. De igual manera, el modelo puede ser m\u00e1s flexible y manejar una amplia gama de consultas y tareas, desde respuestas puntuales a preguntas, hasta la generaci\u00f3n de contenido largo y detallado.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
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\u00bfC\u00f3mo lo aplicamos?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tVolviendo al caso de estudio la t\u00e9cnica de RAG nos permiti\u00f3 entregarle la lista detallada de clientes y los asuntos asociados a cada uno de estos. Los resultados entregados por ChatGPT fueron mucho m\u00e1s precisos en los dos campos problem\u00e1ticos, el 94% de las veces el motor fundacional fue capaz de abstraer la informaci\u00f3n entregada en el audio y esto mejor\u00f3 notablemente la satisfacci\u00f3n de los usuarios al interactuar con el formulario de registros de tiempos.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
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En resumen<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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En resumen, la incorporaci\u00f3n de RAG en nuestro proceso no solo ha transformado la manera en que interactuamos con la IA, sino que tambi\u00e9n ha elevado la calidad y precisi\u00f3n de nuestras respuestas. Al proporcionar un contexto m\u00e1s rico y relevante, hemos dado un paso importante hacia un futuro donde la IA no s\u00f3lo responde, sino que comprende.<\/p>

Pero no se detiene aqu\u00ed: la IA generativa tiene el potencial de revolucionar diversas \u00e1reas de tu negocio. \u00bfTe imaginas c\u00f3mo podr\u00eda mejorar tus procesos? La clave est\u00e1 en ser proactivo y explorar c\u00f3mo puede integrarse en tu estrategia.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Descubre c\u00f3mo la t\u00e9cnica RAG mejora la precisi\u00f3n de la IA generativa, transformando la gesti\u00f3n de tiempos y elevando la satisfacci\u00f3n del usuario en nuestro caso de estudio.<\/p>","protected":false},"author":1,"featured_media":11575,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[23,25],"class_list":["post-10210","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-ai","tag-rag"],"acf":[],"yoast_head":"\nDiversify the functionalities of your tool with Generative Artificial Intelligence, A case study part 2 | Activeone<\/title>\n<meta name=\"description\" content=\"Discover how the RAG technique enhances the accuracy of generative AI, transforming time management and boosting user satisfaction in our case study.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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