Parsing and extracting from resumes with LLMs

Traditional resume parsing methods face challenges due to the variety of formats, industry-specific jargon, and lack of standardization, resulting in only 60-70% accuracy with current systems like Application Tracking Systems (ATS). Refuel offers an innovative solution using a large language model (LLM) approach to enhance resume parsing by achieving 95% accuracy and reducing the time required to build parsers from months to just two days. This method allows for customizable output schemas and significant cost savings compared to conventional systems. Refuel's process involves specifying the context and desired output fields, extracting data using natural language guidelines, and providing feedback to improve accuracy, ultimately enabling scalable and efficient resume parsing in production environments.