Steve: LLM Powered ChatBot for Career Progression
Naveen Mathews Renji, Balaji Rao, Carlo Lipizzi
TL;DR
Steve presents an AI-powered career coaching platform that bridges qualitative interviews with quantitative skill-gap analysis to guide career progression. The system pipelines resume parsing, career-path analysis, skill assessment, and interactive coaching, augmented by a vector-based course retrieval component. Key contributions include a modular, domain-adaptable framework, SME-curated career trees, and an end-to-end implementation leveraging OpenAI APIs, Qdrant, and ontology-driven matching. The approach enables personalized, scalable guidance with practical implications for workforce development and organizational training programs.
Abstract
The advancements in systems deploying large language models (LLMs), as well as improvements in their ability to act as agents with predefined templates, provide an opportunity to conduct qualitative, individualized assessments, creating a bridge between qualitative and quantitative methods for candidates seeking career progression. In this paper, we develop a platform that allows candidates to run AI-led interviews to assess their current career stage and curate coursework to enable progression to the next level. Our approach incorporates predefined career trajectories, associated skills, and a method to recommend the best resources for gaining the necessary skills for advancement. We employ OpenAI API calls along with expertly compiled chat templates to assess candidate competence. Our platform is highly configurable due to the modularity of the development, is easy to deploy and use, and available as a web interface where the only requirement is candidate resumes in PDF format. We demonstrate a use-case centered on software engineering and intend to extend this platform to be domain-agnostic, requiring only regular updates to chat templates as industries evolve.