Interview Simulation & Feedback Generation
An AI-powered interview practice tool using RAG and structured feedback to help users prepare with more realistic, actionable coaching.
- ↗ Built a tool that simulates realistic interviews
- ↗ Generated structured feedback to improve user preparation
- ↗ Used a RAG-powered backend for scenario-specific responses
- ↗ Connected product thinking with career readiness
Overview
The Interview Simulation & Feedback Generation tool was an AI career-readiness product experiment built to help users practice interviews in a more realistic way.
Instead of static question lists, the tool simulated interview scenarios and generated structured feedback that users could apply to improve their responses.
The problem
Interview prep is often generic. Students and job seekers may not know whether their answers are clear, structured, specific, or role-appropriate. They need practice plus actionable feedback.
My role
I built the product as a full-stack AI tool using a RAG-powered backend, LlamaIndex, Python, React, and web technologies. The product connected scenario-specific response generation with feedback structure.
What I learned
Career tools become more useful when they move beyond information and create practice loops. A good AI coach should help users rehearse, reflect, and improve.
PM / APM interview story
Situation: Users needed more realistic interview practice and better feedback.
Task: Build an AI tool that could simulate interviews and provide structured coaching.
Action: I developed a RAG-powered backend and user-facing interface for scenario-specific interview simulation and feedback.
Result: The project became a strong AI career-tool case study and influenced later PathWise/KeyVoid product thinking.