Retrieval-Augmented Generation (RAG) has emerged as a powerful framework that enhances large language models (LLMs) by integrating external retrieval mechanisms. This integration improves factual grounding, reduces hallucinations, and ensures access to evolving information. In human resources (HR) and enterprise AI, where decision-making often relies on unstructured, sensitive, and dynamic data, RAG and memory systems present transformative opportunities. This manuscript expands on prior work by providing a detailed exploration of system architectures, embedding models, vector databases, and synthetic experimental evaluations. It situates RAG applications specifically within recruitment, workforce planning, learning and development, and employee support. Ethical considerations, such as fairness, privacy, and regulatory compliance, are critically assessed. The paper concludes with a forward-looking discussion on multi-agent collaboration, cross-lingual retrieval, and federated memory systems. Together, these discussions highlight RAG’s potential in advancing responsible and explainable enterprise AI.
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Retrieval-Augmented Generation (RAG) and Memory Systems for HR and Enterprise AI
Semantic Scholar · 2025
Abstract
Retrieval-Augmented Generation (RAG) has emerged as a powerful framework that enhances large language models (LLMs) by integrating external retrieval mechanisms. This integration improves factual grounding, reduces hallucinations, and ensures access to evolving information. In human resources (HR) and enterprise AI, where decision-making often relies on unstructured, sensitive, and dynamic data, RAG and memory systems present transformative opportunities. This manuscript expands on prior work by providing a detailed exploration of system architectures, embedding models, vector databases, and synthetic experimental evaluations. It situates RAG applications specifically within recruitment, workforce planning, learning and development, and employee support. Ethical considerations, such as fairness, privacy, and regulatory compliance, are critically assessed. The paper concludes with a forward-looking discussion on multi-agent collaboration, cross-lingual retrieval, and federated memory systems. Together, these discussions highlight RAG’s potential in advancing responsible and explainable enterprise AI.