# Zara: An LLM-based Candidate Interview Feedback System

arXiv: https://arxiv.org/abs/2507.02869 · HTML: https://mlsanity.com/paper/2507.02869/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2025.

## Built on by (most cited first, 1 of 1)

- [CoMAI: A Collaborative Multi-Agent Framework for Robust and Equitable Interview Evaluation](https://mlsanity.com/paper/2603.16215/) (2026)

## Abstract (arXiv)

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.