Building Jobly: Semantic Job Matching with RAG and Vector Embeddings

Jobly is an AI-powered gig marketplace developed to enhance job matching in the gig economy by employing semantic search, vector embeddings, and Retrieval-Augmented Generation (RAG) instead of traditional keyword matching. This innovative approach was designed during the Hugging Face Winter Hackathon 2025 to address the limitations of keyword-based systems, which often miss synonyms and context, leading to mismatches. Jobly's three-tier matching architecture begins with TF-IDF for basic matches, progresses to vector embeddings for semantic understanding, and culminates with RAG to incorporate metadata and optimize results. This system uses models like LlamaIndex and HuggingFace to create a robust platform that understands the meaning of job descriptions and worker profiles, providing a more precise and context-aware matching process. Through this integration, Jobly achieves high precision and explainability without relying on expensive infrastructure, offering a scalable solution for matching gig workers with suitable opportunities.

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