Home > AI Solutions > Gen AI > White Papers > HR-Assist: Proof of Concept RAG-Based Matching Assistant > Introduction
Human Resources (HR) professionals are the backbone of any organization, playing an indispensable role in managing and nurturing the most valuable asset of any company—its people. They oversee a wide range of functions, from recruitment and onboarding to employee relations and performance management. Among their many functions is the crucial task of managing rotation programs, which have become a staple in organizations that thrive on empowering new grads to jumpstart their early career journey. The premise of these programs is to ensure that new grads can utilize and showcase their freshly cultivated skills while exploring the organization's diverse departments and teams' operations and workflows.
At first glance, what seems like a simple process of matching employees to their respective teams actually consists of numerous sub-processes, each requiring meticulous attention to detail. HR professionals must consider a multitude of tasks, such as gathering and organizing data about employees' skills, experience, availability, preferences, and geographical constraints. If an immediate match is not found, the process loops back, requiring adjustments to the criteria or exploration of alternative solutions, which inevitably adds both time and complexity to the task. The highly repetitive and manual nature of HR tasks often slows decision-making and increases the workload on HR professionals, usually leading to inefficiencies and suboptimal matches. The likelihood of a manager landing on the best-fit person for their project is significantly reduced, affecting project results and team member satisfaction.
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At Dell Technologies, we addressed these challenges by leveraging our advanced technologies, as elucidated in the reference design paper, Dell Scalable Architecture for Retrieval-Augmented Generation (RAG) with NVIDIA Microservices. Building on this foundation, our team containerized a solution using Docker—as detailed in the RAG POC How-to Guide—to create a more accessible and manageable solution for proof-of-concept purposes with platform agnosticism, consistent performance, and scalability. This evolution has allowed us to detail how that same RAG chatbot method—initially created to enhance information retrieval and conversational AI—can help us streamline HR processes. With this proof-of-concept deployment, we have successfully demonstrated the applicability of our solutions in real-world scenarios, furthering our mission to automate and improve workflow processes across industries.