Retrieval-Augmented Generation to Generate Knowledge Assets and Creation of Action Drivers

Retrieval-Augmented Generation to Generate Knowledge Assets and Creation of Action Drivers

This article explores the application of Retrieval-Augmented Generation (RAG) to enhance the creation of knowledge assets and develop actionable insights from complex datasets. It begins by contextualising the limitations of large language models (LLMs), notably their knowledge cut-offs and hallucin...

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Bibliographic Details
Main Authors: Antony James, Marcello Trovati, Simon Bolton
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Applied Sciences
Subjects:
RAG
Retrieval-Augmented Generation
LLM
large language models
AI
artificial intelligence
Online Access:https://www.mdpi.com/2076-3417/15/11/6247
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