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September 27, 2025International Journal of Scientific Research in Computer Science Engineering and Information TechnologyOpen Access

Retrieval-Augmented Generation (RAG) and Memory Systems for HR and Enterprise AI

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Authors

SLSneh LataGenmab (United States)

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Overview

This exploration highlights retrieval-augmented generation's role in enterprise AI, emphasizing ethical considerations and memory systems.

Key Points

  • Retrieval-augmented generation improves decision-making in HR by integrating external data sources, enhancing model accuracy.
  • The integration of memory systems and RAG can significantly reduce hallucinations in large language models and improve factual grounding.
  • Applications of RAG in recruitment and employee support show potential for transforming HR practices and improving overall efficiency.
  • Ethical assessments of privacy and fairness are crucial as organizations adopt RAG to ensure responsible enterprise AI usage.

Cite This Study

Sneh Lata (2025) studied this question.

synapsesocial.com/papers/68d7b3ddeebfec0fc523677bhttps://doi.org/10.32628/cseit25111702
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Retrieval-Augmented Generation (RAG) Systems in Production: Software Architecture Strategies for Enterprise-Grade AI Applications2025
  2. 2Advances in Retrieval-Augmented Generation (RAG) and Related Frameworks2025
  3. 3Enhancing Data Engineering And Knowledge Discovery With Retrieval-Augmented Generative AI2023
  4. 4Retrieval-Augmented Generation (RAG) in Healthcare: A Comprehensive Review2025 · 23 citations
  5. 5Retrieval-Augmented Generation (RAG) in Healthcare: A Comprehensive Review2025 · 79 citations