Tailored Resume Generation for Job Applications using RAG with LLMs

Patra, Komal Prakashchandra (2024) Tailored Resume Generation for Job Applications using RAG with LLMs. Masters thesis, Dublin, National College of Ireland.

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Abstract

In an era of digital job applications, it is very crucial that the resume should stand out from other applications. The generic traditional resume fails to effectively highlight the skills for diverse job specifications. The research introduces a novel method of tailoring the resume that aligns with job description that leverages Retrieval Augmented Generation (RAG) with Large Language Models (LLM). The resume parsing is the information retrieval step to extract each section such as Education, Experience, skills, etc. which has also been performed using LLMs such as LLAMA-3, Mixtral and Gemma model. For retrieval, the resume data has been stored in Pinecone (as chunked PDF data) and MongoDB (entire JSON resume) vector databases. The Re-ranking techniques such as BM25 and Cross Encoder has been used to enhance the performance of the retriever. In this study, the BM25 which is a keyword-based search outperforms the cross-encoder. The prompt engineering is used to instruct the model to generate the tailor resume with one-shot approach where one example has been given a model to better understand. The variants of LLAMA-3 were used with ChatGroq to mitigate the computational load and faster inference. To provide the memory to the LLM models, the conversational buffer memory of langchain has been utilised by storing the last five previous chat history, so that the user can keep on refining the tailor resume till they get satisfied. The efficiency of the methodology is highlighted through a series of evaluations such as RAGAs Metrics, BERTScore, and Custom metrics such as Job Alignment and Content Preservation. By comparing the cosine similarity for content preservation between LLM and RAG with LLM are 72% and 89% respectively. The RAG with LLM outperforms compared to using only LLM to generate the tailored resumes. The Chatbot interface is designed for a user to have a seamless and highly user-friendly experience.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Tomer, Vikas UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing H Social Sciences > HD Industries. Land use. Labor > Issues of Labour and Work > Job Seeking Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	25 Aug 2025 09:52
Last Modified:	25 Aug 2025 09:52
URI:	https://norma.ncirl.ie/id/eprint/8610

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