Interview Simulation & Feedback Generation
An AI-powered interview practice tool using RAG and structured feedback to help students and job seekers practice with more realistic coaching loops.
- ↗ Built a tool that simulates realistic interview practice.
- ↗ Generated structured feedback to help users improve answers.
- ↗ Used a RAG-powered backend for scenario-specific interview responses.
- ↗ Connected AI product thinking with career readiness and coaching workflows.
Overview
The Interview Simulation & Feedback Generation tool was an AI career-readiness product experiment built to help users practice interviews in a more realistic and actionable way.
Instead of giving users static question lists, the tool simulated interview scenarios and generated structured feedback that could help users improve their responses.
The goal was to create a practice loop:
answer → feedback → reflection → better answer.
The problem
Interview preparation is often too generic.
Students and job seekers may have access to common interview questions, but they may not know whether their answers are clear, structured, specific, role-appropriate, or convincing.
The real gap is feedback.
Users need help understanding:
- Did I answer the question directly?
- Was my response structured?
- Did I include enough evidence?
- Did I sound confident?
- Did I connect my experience to the role?
- What should I improve next?
The product was designed to make interview practice more active and more useful.
My role
I built the product as a full-stack AI tool using a RAG-powered backend, LlamaIndex, Python, React, and web technologies.
My work included:
- Designing the interview simulation flow
- Connecting scenario-specific prompts to the backend
- Building structured feedback generation
- Creating the user-facing interface
- Exploring how RAG could support more contextual interview coaching
- Thinking through how career tools can create practice loops rather than only information pages
Product approach
The key product decision was to treat interview preparation as a coaching workflow.
A useful AI interview tool should not only ask questions. It should help users understand how to improve.
That meant the product needed to support:
- Role-specific or scenario-specific practice
- Clear prompts
- Structured feedback
- Actionable improvement suggestions
- Repeated attempts
- Better preparation confidence
The product direction later connected naturally to the broader KeyVoid and PathWise vision: helping students move from uncertainty to readiness.
Visual proof
AI interview simulation and feedback dashboard.
Structured interview feedback output.
RAG-powered interview practice flow.
What I learned
Career tools become more useful when they create practice loops.
A good AI coach should help users rehearse, reflect, and improve. It should not only generate content. It should help people become more prepared for the next real interaction.
This project helped me understand that career-readiness products need to combine AI output with behavioral improvement. The strongest value is not the answer the model gives. It is the improvement the user can make after receiving it.