AI Mock Interview Coach

An AI-powered mock interview platform that generates interview questions, evaluates answers, and provides personalized feedback in real time.

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Inspiration

Preparing for interviews can be intimidating, especially for students and early-career developers who often lack access to realistic interview practice and detailed feedback. I wanted to build a platform that simulates an interview experience using AI, allowing users to practice technical, behavioral, and HR interviews while receiving instant feedback to improve their performance.

What it does

AI Mock Interview Coach is a backend application that creates a complete AI-powered interview workflow.

How we built it

The project was built using:

  • FastAPI for the REST API
  • PostgreSQL for persistent storage
  • SQLAlchemy ORM for database modeling
  • Pydantic for request and response validation
  • Google Gemini API for question generation and AI feedback
  • Python as the primary programming language

Challenges we ran into

Building this project involved solving several real backend engineering challenges.

Some of the biggest challenges included:

  • Designing database relationships between interview sessions, questions, and answers
  • Using UUIDs as primary keys while maintaining foreign key relationships
  • Validating API requests and responses with Pydantic
  • Handling invalid JSON responses from the AI model
  • Managing Gemini API rate limits and error handling
  • Debugging SQLAlchemy relationships and validation issues
  • Building a multi-step interview workflow while maintaining session state

Each challenge improved my understanding of backend development and API design.

Accomplishments that we're proud of

  • Successfully integrated Google's Gemini API into a FastAPI application
  • Built a complete multi-step interview workflow
  • Designed a normalized PostgreSQL database
  • Implemented clean RESTful API endpoints
  • Added AI-generated interview feedback and scoring
  • Completed an end-to-end backend project from planning to deployment-ready code

What we learned

This project strengthened my understanding of:

  • FastAPI architecture
  • SQLAlchemy ORM
  • PostgreSQL
  • Pydantic validation
  • Environment variable management
  • AI API integration
  • Error handling

  • Designing scalable backend applications

    What's next for AI Mock Interview Coach

    Future improvements include:

  • User authentication with JWT

  • Resume upload and AI analysis

  • Voice-based interview practice

  • React frontend dashboard

  • Interview history and analytics

  • PDF interview reports

  • Docker deployment

  • CI/CD with GitHub Actions

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