Contextualized Information Generation and Retrieval: An Advanced RAG-Based Approach to Natural Language Processing

Generación y Recuperación de Información Contextualizada: Un Enfoque Avanzado Basado en RAG para el Procesamiento del Lenguaje Natural

This article explores in depth the integration of advanced machine learning techniques using the Retrieval Augmented Generation (RAG) methodology. The dual architecture that combines information retrieval and generation processes is analyzed, highlighting its impact on the training of natural language models. Likewise, specialized variants such as Corrective RAG and Advanced RAG are presented, which incorporate real-time feedback and optimization mechanisms. Also included a mention of the JurislibreIA product, developed by the Sensorama research group, exemplifying practical applications in complex domains such as the legal one. The study is based on implementation examples in Python, explanatory diagrams and a critical review of relevant sources, offering a complete guide for researchers and developers interested in promoting innovative solutions based on RAG.

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