CV Score Master
Description
The "CV Score Master" project represents a sophisticated web application designed to evaluate and rank CVs (resumes) by matching them to job descriptions. This ambitious undertaking involved the application of advanced language models and Prompt Engineering techniques. The system encompassed a wide array of features, from CV feature extraction to sentiment analysis and topic modeling, all grounded in the capabilities of Large Language Models (LLMs).
Features
- CV Scoring and Ranking: The core functionality of the application revolved around scoring and ranking CVs based on their alignment with specific job descriptions.
- Large Language Models (LLMs): We harnessed state-of-the-art LLMs, such as GPT-3, for a deeper understanding of both CVs and job descriptions.
- Prompt Engineering: To fine-tune the LLMs for this specialized task, we employed Prompt Engineering to create relevant queries and interactions.
- CV Feature Extraction: The system extracted crucial features from CVs, enabling it to make informed assessments.
Technologies
Lessons Learned
- The Power of LLMs: Large Language Models proved to be invaluable for tasks requiring deep text understanding, opening up new possibilities in CV evaluation.
- Tailoring for Specific Domains: Prompt Engineering is a highly adaptable tool that can be customized to suit various specialized tasks, as demonstrated in our project.
- Continuous Learning: The project underscored the need for ongoing learning and adaptation to stay current with the evolving field of NLP and AI.