Interviewer.ai

Full-stack application that generates 3 tailored interview questions based on a job title and difficulty tier, powered by Groq's LLaMA 3.3 70B model. The platform allows users to enter a role such as Customer Success Manager, Frontend Engineer, or Product Manager and instantly receive thoughtful, role-specific interview questions generated in real time.

The project demonstrates:

• Full-stack TypeScript architecture
• AI API integration
• REST API design
• Schema validation
• Interactive Swagger documentation
• Modern UI/UX patterns
• Clean project organization

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Features

• Generate 3 AI-powered interview questions
• Groq LLM integration
• Vanilla TypeScript frontend
• Express + TypeScript backend
• Swagger API documentation
• Zod schema validation
• Bento-style responsive UI
• Copy-to-clipboard interaction
• Loading and latency states
• Environment-based configuration
• Production-ready structure

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Tech Stack

Frontend

• TypeScript
• HTML5
• CSS3
• Vite

Backend

• Node.js
• Express
• TypeScript
• Zod
• Swagger UI

AI

• Groq API
• Llama 3 / Mixtral models

Tooling

• Yarn
• Biome
• dotenv

Deployment

• Render

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Project Structure

interview-generator/

├── src/
│   ├── main.ts          ← Frontend logic (fetch, DOM, latency tracking)
│   └── style.css        ← Bento grid UI styles
├── index.html           ← App shell
├── server.ts            ← Express API + Groq integration
├── vite.config.ts       ← Vite + proxy config
├── tsconfig.json        ← TypeScript config
├── package.json         ← Dependencies and scripts
├── yarn.lock            ← Locked dependency versions
├── .env.example         ← Environment variable template
├── .gitignore
└── README.md

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Architecture Overview

flowchart TD
    A[Frontend - TypeScript + Vite]
    B[Backend - Express API]
    C[Groq API]
    
    A -->|POST /api/generate-questions| B
    B -->|Prompt Request| C
    C -->|AI-generated questions| B
    B -->|JSON response| A

Loading

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Quick Start

Prerequisites

• Node.js v18+
• Yarn
• Groq API key (free, no credit card required)

Create a free API key at:

https://console.groq.com

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Setup

Clone the repository:

git clone https://github.com/azukauteh/interview-generator.git
cd interview-generator

Install dependencies:

yarn install

Create environment variables:

cp .env.example .env

Add your API key to .env:

GROQ_API_KEY=your_groq_api_key_here
PORT=3000
NODE_ENV=development

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Running the Application

Start the development server:

yarn dev

Local URLs

Service	URL
Frontend App	http://localhost:5173
Backend API	http://localhost:3000
Swagger Docs	http://localhost:3000/docs

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Production Build

Build the application:

yarn build

Start production server:

yarn start

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API Reference

POST /api/generate-questions

Generate AI-powered interview questions based on a job title.

Request Body

Field	Type	Required	Description
jobTitle	string	✅	Job title (2–100 characters)
difficultyTier	Standard | Advanced	❌	Defaults to Standard

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Example Request

{
  "jobTitle": "Customer Success Manager",
  "difficultyTier": "Advanced"
}

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Example Response

{
  "questions": [
    "How do you identify early churn signals across a large portfolio?",
    "Describe a time you influenced product roadmap based on customer feedback.",
    "How do you balance reactive support with proactive success management?"
  ]
}

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Validation & Error Handling

The backend validates all incoming requests using Zod schemas.

Examples:

• Empty job titles are rejected
• Invalid payload structures return proper HTTP errors
• AI provider failures are gracefully handled

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Swagger Documentation

Interactive API documentation is available at:

http://localhost:3000/docs

Swagger provides:

• Endpoint testing
• Request/response schemas
• API exploration
• Error response documentation

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Development Scripts

Development

yarn dev

Build

yarn build

Start Production

yarn start

Type Checking

yarn typecheck

Linting

yarn lint

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Deployment

The application is deployed using:

• Render

Ensure the following environment variables are configured in production:

GROQ_API_KEY=
PORT=
NODE_ENV=production

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Contributing

1. Fork the repository
2. Create a feature branch

git checkout -b feat/your-feature

3. Commit your changes

git commit -m "feat: your feature"

4. Push your branch

git push origin feat/your-feature

5. Open a Pull Request

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Screenshots

Live Demo

https://www.loom.com/share/3400e33a76f04671991e17ac86eca70e

Future Improvements

• Multiple AI provider support
• Question difficulty tuning
• Interview answer evaluation
• Saved interview sessions
• PDF export support
• Streaming AI responses

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License

MIT

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Built with Groq · Express · TypeScript · Vite