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@article{202617,
        author = {Rudra Bokil and Aditya Watode and Shailesh Thaware},
        title = {AI-Powered Resume Analyzer and Career Assistance System Using Large Language Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {11287-11290},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202617},
        abstract = {Increasing reliance on Application Tracking System (ATS) has created an array of complications for job seekers which include limited feedback, keyword dependency, or lack of personalization. Traditional tools often fail to provide detailed insights or actionable feedback. This leaves the users unsure of how to actually improve their resumes. This paper explores an AI-powered Resume Analyzer and chat assistance that uses large language models (LLMs) to evaluate user resumes and generate feedback. The system performs resume analysis by identifying strengths, weaknesses, and generating an ATS-style score based on predefined evaluation criteria. Furthermore, AI-driven chat assistance allows users to ask questions related to resume improvement, career guidance, ATS optimization. This work demonstrates a feasible alternative to traditional tools by integrating LLMs for personalized carrier assistance.},
        keywords = {Resume Analysis, Applicant Tracking System (ATS), Large Language Models (LLMs), Natural Language Processing (NLP), AI Chat Assistant, Career Recommendation Systems.},
        month = {May},
        }