Leveraging RAG for Effective Prompt Engineering in Job Portals

Traditional recruitment methods are time-consuming and heavily reliant on manual processes, leading to inefficiencies. This paper explores the integration of AI technologies into a job portal to streamline the hiring process, focusing on AI’s ability to automatically parse and extract information from resumes and job descriptions to match candidates with relevant job opportunities. The platform leverages Large Language Models (LLMs) to extract key information, such as skills, education, and work experience, from both resumes and job descriptions. Fine-tuning through prompt engineering ensures accurate extraction, even when data fields are incomplete or missing. To further enhance matching accuracy, Retrieval-Augmented Generation (RAG) techniques are employed. These mechanisms retrieve relevant information from a structured skills database to provide context, which, combined with the generative capabilities of LLMs, enables more contextually accurate matches. Candidates receive personalized job recommendations based on the information extracted from their resumes, while employers can post job descriptions and use AI-driven tools to match candidates. This approach of using contextual prompts not only improves matching accuracy but also reduces computational time, eliminating the need for custom models tailored specifically to resumes or job descriptions.