Cover Letter Generator - Semantic Matching and AI Generation
MY ROLES
TIMELINE
2021
TOOLS USED
LLMs, Vector Embeddings, Generative AI, Python, Content Generation, Semantic Matching, NLP, Career Development, Personalization, OpenAI
DESCRIPTION
This project, one of my first LLM production deployments and a component of DataRebel.io, automatically generates customized cover letters by analyzing the relevance between a user's portfolio and job postings. The system creates vector embeddings of user projects and job descriptions, ranks the relevance of each experience to the target position, and then generates a tailored cover letter that highlights the most relevant skills and experiences using templates and contextual information.
OUTCOMES
- Developed one of the first production LLM applications in the portfolio generator space
- Reduced cover letter creation time from hours to seconds while maintaining personalization
- Created a system that intelligently highlighted the most relevant experiences for each job application
FEATURES
Vector embedding generation, project-to-job relevance ranking, template-based content generation, personalized experience highlighting, integration with the DataRebel.io platform
CHALLENGES
Ensuring that generated content was both personalized and professionally appropriate. Creating effective embeddings that captured the nuances of both user projects and job requirements. Developing a ranking algorithm that identified truly relevant experiences rather than surface-level keyword matches.
APPROACH
Generated vector embeddings for all user projects and job descriptions. Implemented similarity scoring to rank a user's projects and experiences by relevance to specific job postings. Developed context-aware templates that incorporated the most relevant user experiences. Integrated the system with DataRebel.io to provide users with instantly generated, customized cover letters for any job they were interested in applying to.