CV Summary and Professional Recommendations Using RAG and NLP - Sarker, Utsha; Biswas, Archy; Saurabh; Vaishnav, Lalit; Rathod, Myla Vizwal (International Journal of Engineering and Management Research, 2025)
Job searching can be a very tedious affair as one has to tailor-make resumes to fit every job posting. This article provides an AI-driven approach that will cut down the fuss of making resumes, choosing keywords, and matching them precisely with job postings through RAG and NLP. The system merges a transformer-based LLM with semantic search and vector embeddings to quickly identify the roles, qualifications, experience, and skills that users highlight in their extracts. Keyword extraction also aligns with job market trends to increase application success rates. The job matching module uses FAISS-based semantic search, ranking opportunities by relevance and skill match. Mass-scale experimentation with different sets of resume and job posting data confirms the effectiveness of the system with an astonishing 92% accuracy in job matching and skill extraction. By bridging the gap between recruiters and job candidates, the process streamlines candidate profiling, making the hiring process more accurate, precise, and data-driven.