LLM-Powered Cold Email Generator for Job Applications

1. What This Project Does

This project is an AI-powered job application assistant designed to streamline the process of cold emailing for job applications. It takes a job posting URL as input and instantly generates a personalized cold email tailored to that specific job. The goal is to reduce the manual effort typically required in outreach and make the process intelligent, fast, and highly relevant to each opportunity.

2. Project Workflow

  • When a user provides a job description link, the assistant dynamically scrapes job details from the corresponding career page using LangChain’s WebBaseLoader.
  • Used prompt engineering to convert the scraped, unstructured job data into structured JSON (role, experience, skills, description).
  • Matched job-required skills with relevant portfolio projects using ChromaDB’s vector store for semantic similarity.
  • Implemented a Retrieval-Augmented Generation (RAG) workflow to generate context-aware emails based on job descriptions and user experience.
  • Chained LangChain components to automate the complete flow from data extraction to personalized email generation.

3. Results and Impact

This assistant demonstrated real-world productivity improvements by significantly reducing the time and manual effort involved in cold outreach. The intelligent use of large language models and automated workflows helped streamline the job application process and made personalized communication far more efficient.

4. Summary

The project combines advanced AI capabilities with practical automation to simplify job outreach. By scraping job listings, structuring the data intelligently, matching it with relevant experience, and generating custom cold emails through a smooth, automated workflow, it delivers a powerful tool for job seekers looking to scale their outreach efforts without sacrificing personalization.

Tech Stack

  • Groq-hosted LLaMA 3.3-70B
  • LangChain
  • Prompt Engineering
  • Streamlit
  • WebBaseLoader
  • ChromaDB
  • Retrieval-Augmented Generation (RAG)