All GuidesGuide 05
Intermedio • ~50min

RAG — Retrieval-Augmented Generation

Ground AI in your documents with RAG. Learn chunking strategies, vector embeddings, and hallucination reduction techniques.

Haz tus respuestas de IA más precisas anclándolas en tus propios datos.

Objetivos

01Comprender el principio RAG (Retrieval-Augmented Generation)
02Implementar un pipeline RAG simple con base de datos vectorial
03Optimizar estrategias de chunking y calidad de recuperación

Contenido

Section 01
Introducción a RAG: ¿por qué integrar datos externos?
Section 02
Vectorización, embeddings y recuperación semántica.
Section 03
Ejercicio: indexar un conjunto de documentos y consultar con RAG.
Section 04
Optimización: chunking, metadatos y reranking.

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Habilidades

Implementar un pipeline RAG con base de datos vectorial.Fragmentar e indexar documentos para recuperación óptima.Mejorar calidad de recuperación con metadatos y reranking.

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Preguntas frecuentes

¿Qué es el módulo "RAG — Retrieval-Augmented Generation"?+

"RAG — Retrieval-Augmented Generation" es un módulo de formación en línea de nivel Intermedio • ~50min. Ground AI in your documents with RAG. Learn chunking strategies, vector embeddings, and hallucination reduction techniques.

¿Hay requisitos previos para este módulo?+

Sí, recomendamos completar el Módulo 4 antes de comenzar este módulo.

¿Este módulo es gratuito?+

Sí, este módulo es completamente gratuito y accesible sin suscripción de pago.

¿Qué aprenderé en este módulo?+

Implementar un pipeline RAG con base de datos vectorial.. Fragmentar e indexar documentos para recuperación óptima.. Mejorar calidad de recuperación con metadatos y reranking..

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