Abstract:
Human Capital (HC) departments are often burdened with repetitive questions from
employees regarding policies, benefits, procedures, and other administrative matters, so these
routine tasks can divert valuable time and resources from strategic HC functions. This study
aims to develop and implement an intelligent system that is capable of delivering accurate,
relevant and context-aware responses through domain-specific chatbot using
Retrieval-Augmented Generation (RAG) architecture, specifically designed to assist
employees in the company policies domain. The system integrates a large language model
developed by OpenAI with a curated internal knowledge base consisting of company policies
and Human Capital-related documents. The RAG framework was selected for its ability to
combine information retrieval with generative capabilities, enabling dynamic responses based
on factual content. The findings show that the proposed chatbot significantly increased the
accuracy and relevance of responses compared to traditional FAQ systems and general
purpose chatbots. The domain-specific RAG chatbot demonstrates strong potential to
enhance the operational efficiency of HC divisions. By leveraging OpenAI’s advanced
language model, the system represents a significant step forward in intelligent employee
support and paving the way for more intelligent and responsive Human Capital services.
