Zara: An LLM-based Candidate Interview Feedback System

TL;DR

Zara is an AI system utilizing GPT-4o and RAG to provide personalized, scalable interview support and feedback, improving candidate experience and transparency in recruitment processes.

Contribution

The paper presents Zara, a novel LLM-based system that enhances candidate feedback and interview preparation through innovative AI techniques and open-sourcing its methodology.

Findings

Effective personalized interview simulations generated by Zara.

Automated, structured feedback improves candidate understanding.

Open-sourced approach promotes transparency and reproducibility.

Abstract

This paper introduces Zara, an AI-driven recruitment support system developed by micro1, as a practical case study illustrating how large language models (LLMs) can enhance the candidate experience through personalized, scalable interview support. Traditionally, recruiters have struggled to deliver individualized candidate feedback due to logistical and legal constraints, resulting in widespread candidate dissatisfaction. Leveraging OpenAI's GPT-4o, Zara addresses these limitations by dynamically generating personalized practice interviews, conducting conversational AI-driven assessments, autonomously delivering structured and actionable feedback, and efficiently answering candidate inquiries using a Retrieval-Augmented Generation (RAG) system. To promote transparency, we have open-sourced the approach Zara uses to generate candidate feedback.