Automated Email Generation using Large Language Models for Client Communication Efficiency

This study aims to automate job portal monitoring, job data extraction, and cold email generation to enhance efficiency in software service companies. The system integrates LLaMA 3.1 for natural language processing, ChromaDB for efficient job data retrieval, LangChain for structured prompt engineering, and Streamlit for an interactive front-end interface. The methodology involves web scraping job postings, preprocessing and structuring job descriptions, matching them with user portfolios using vector embeddings, and generating personalized emails customized to job relevance. ChromaDB ensures fast retrieval of relevant job postings, while LangChain optimizes prompt engineering to enhance email personalization. The system's performance was evaluated based on processing time, similarity scoring, and email quality, demonstrating significant improvements in workflow automation, outreach efficiency, and paper acquisition. Results indicate that AI-powered automation streamlines workflow optimization, enhances email generation efficiency, and provides a competitive edge in responding to job opportunities.