## 1 Introduction  
The growing dependence on artificial intelligence (AI) to enhance human functions, especially in customer service settings like contact centers, has attracted considerable interest in recent years. Call center workers frequently need to swiftly adjust to new projects and intricate client inquiries, highlighting the urgent demand for efficient, scalable technologies that facilitate rapid learning and problem solving in these dynamic settings. Virtual assistants utilizing large language models (LLMs) provide an innovative resolution to this issue by delivering instant access to pertinent information, hence minimizing the time needed to address client inquiries. AI technologies in these environments enhance agent efficiency while responding to broader industry movements to automate customer service and reduce human error. This research presents a virtual assistant tailored for call center agents, utilizing the Retrieval-Augmented Generation methodology and the VERTEX AI Palm-2 model. The main objective is to evaluate the effectiveness of VA in enhancing problem solving by using contextual data from literature and educational methodologies. Additionally, the study examines the possible detrimental impacts of erroneous VA responses on end users. The research aims are situated within the larger framework of AI's contribution to enhancingoperational efficiency and reducing hazards linked to dependence on machine-generated data. This research tackles the special requirement for contextual accuracy and reliability in the high-stakes customer service domain, contrasting with many studies concentrating on enhancing LLMs for generalized tasks. This work is significant for its potential to connect human expertise with AI aid, improving the speed and accuracy of responses in areas where human agents may lack domain knowledge. Recent studies on AI in customer service indicate substantial advancements in utilizing LLMs, primarily via one-shot and few-shot learning techniques, enabling models to excel with limited training data. Nevertheless, obstacles persist, particularly in guaranteeing that LLMs deliver precise, contextually pertinent responses while circumventing prevalent issues such as hallucinations and inaccuracies in handling organized data, such as tables. Current methods frequently inadequately resolve these problems, especially in multilingual contexts where understanding is essential. This study extends previous research by employing an enhanced RAG approach within the context of a student office, illustrating a practical application of LLMs in specialized settings. There are four contributions from this paper:  
* Utilization of the RAG method in conjunction with VERTEX AI for specific contexts: The research introduces a novel use of the RAG approach combined with the VERTEX AI Palm-2 model to develop a VA tailored for university contact centers. This approach is utilized in a specialized setting (university student office), demonstrating the adaptability of AI models to domain-specific tasks where context and precision are essential. This expands the use of RAG and LLMs beyond broad uses, illustrating their capability to address highly specialized issues. * Mitigation of hallucinations in AI responses by contextual learning: A notable challenge with large language models, such as Palm-2, is the production of hallucinations--erroneous or deceptive information that lacks foundation in the given context. This paper illustrates how the RAG approach, in conjunction with contextual learning, can diminish the incidence of hallucinations, particularly when addressing organized data such as tables. The significance of this is undeniable in the deployment of AI within professional settings, where precision is essential. * Evaluation of the adverse effects of AI errors on end users: The research investigates the possible damage inflicted by erroneous or deceptive responses from the VA.