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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| Autors principals: | , , |
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| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
MDPI AG
2025-06-01
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| Col·lecció: | Applied Sciences |
| Matèries: | |
| Accés en línia: | https://www.mdpi.com/2076-3417/15/11/6247 |
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