From Resume to Cover Letter Using AI and LLM, with Python and Streamlit

This blog post demonstrates how to create a cover letter AI generator using Python and readily available Large Language Models (LLMs), avoiding the expense of building one from scratch. Many have successfully used this approach to build businesses, but this tutorial focuses on the technical implementation. The code is available on GitHub.

Pep Guardiola's shift in football strategy from "Tiki-Taka" to a more direct approach mirrors a change in the job market. While networking remains effective, online platforms like LinkedIn and Indeed have altered the landscape. AI further enhances this, offering tools to tailor resumes and cover letters. While many companies sell these services, the underlying AI is often similar to publicly available LLMs like ChatGPT or Gemini. This tutorial shows how to build a comparable tool cheaply.

The goal is to create a simple, inexpensive "resume assistant" focusing on cover letters. You input your resume and job description, and the tool generates a cover letter ready for use.

LLMs are used for two key tasks:

1. Document Parsing: An LLM extracts relevant information (name, experience, skills) from the resume and saves it as a JSON file. Llama or Gemini are cost-effective options.
2. Cover Letter Generation: A second LLM uses the parsed resume data and job description to create the cover letter. Again, Llama or Gemini are suitable choices.

The Python implementation uses JSON files for prompts to maintain consistency and readability. The resume_parser_api.json file contains the prompt for the document parsing LLM:

"You are a resume parser. You will extract information from this resume and put them in a JSON file. The keys of your dictionary will be first_name, last_name, location, work_experience, school_experience, skills. In selecting the information, keep track of the most insightful."

The cover_letter_api.json file contains the prompt for the cover letter generation LLM:

"You are an expert in job hunting and a cover letter writer. Given a resume JSON file, the job description, and the date, write a cover letter for this candidate. Be persuasive and professional. Resume JSON: {resume_json}; Job Description: {job_description}, Date: {date}"

The Python code (in cover_letter.py) uses these prompts, along with the user's resume and job description, to interact with the chosen LLM API (e.g., Llama API). The process involves loading the resume, parsing it, adding the job description, and then generating the cover letter.

A Streamlit web app provides a user-friendly interface for uploading the resume, entering the job description, and generating the cover letter.

While AI-generated cover letters are effective, it's crucial to add a personal touch to avoid a generic tone. The author mentions similar projects by Balaji Kesavan, Randy Pettus, and Juan Esteban Cepeda, highlighting the innovative use of LLMs in job hunting. The author, Piero Paialunga, concludes by providing contact information and links to his LinkedIn profile, newsletter, and Upwork page.

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