How I Built an AI Interview Preparation Platform

zaki kasmi — Fri, 17 Jul 2026 02:11:46 +0000

Landing interviews is difficult. Passing them is even harder.

Over the past year, I noticed the same pattern among developers and job seekers:

They knew the technology.
They had solid projects.
Their resumes were decent.
Yet they struggled during interviews.

Most interview preparation tools focused on quizzes or generic questions. I wanted to build something that felt closer to a real technical interview—one that adapts to the role, asks relevant questions, and provides actionable feedback.

This led me to build an AI-powered interview preparation platform.

The Problem

Traditional interview preparation has several limitations:

Generic question banks
Little personalization
No understanding of your resume
No adaptation to the job description
Limited feedback beyond "correct" or "incorrect"

Real interviews are dynamic. Every interviewer asks different questions based on your experience, projects, and the role you're applying for.

I wanted to recreate that experience using AI.

The Goal

The platform should be able to:

Analyze a job description
Understand the candidate's resume
Generate role-specific interview questions
Conduct conversational mock interviews
Evaluate answers
Identify weak areas
Recommend improvements

Instead of memorizing hundreds of questions, candidates practice the questions they're actually likely to receive.

Choosing the Tech Stack

I wanted something scalable and easy to iterate on.

Frontend

Next.js
React
TypeScript
Tailwind CSS

Backend

NestJS
PostgreSQL
Prisma
Redis

AI

Large Language Models
Prompt engineering
Structured JSON outputs
Streaming responses

The backend exposes APIs for interview generation, scoring, feedback, and progress tracking.

Step 1: Understanding the Candidate

The first challenge was building context.

Rather than asking random questions, the AI first understands:

Resume
Skills
Experience
Projects
Target role
Company
Seniority level

This context becomes the foundation for every interview session.

Step 2: Understanding the Job

Every job description is different.

The AI extracts:

Required skills
Preferred skills
Responsibilities
Industry
Seniority
Technologies

This allows interview questions to closely match the actual position.

Step 3: Dynamic Question Generation

Instead of storing thousands of hard-coded questions, the platform generates them on demand.

For example, a backend engineer might receive questions about:

System design
REST APIs
Database optimization
Docker
Kubernetes
Authentication
Scaling

A frontend developer would instead receive questions about:

React
Performance
Accessibility
State management
Rendering
Testing

Each interview becomes unique.

Step 4: Evaluating Answers

The most challenging part wasn't generating questions.

It was evaluating answers fairly.

The AI scores answers based on multiple factors:

Technical accuracy
Completeness
Communication
Depth of explanation
Confidence
Missing concepts

Instead of simply giving a score, it explains why the answer could be improved.

This type of feedback is far more useful than a simple pass/fail result.

Step 5: Personalized Feedback

At the end of an interview, candidates receive a detailed report covering:

Strengths
Weaknesses
Knowledge gaps
Recommended topics
Practice suggestions

This transforms interview preparation into a continuous learning process rather than a one-time assessment.

Lessons Learned

Building AI products taught me several important lessons.

Context is everything

Large language models perform dramatically better when given structured, relevant context.

Prompt engineering matters

Small prompt changes often produced significant improvements in response quality.

Users value feedback more than scores

People don't just want to know how they performed.

They want to know how to improve.

AI should assist, not replace

The goal isn't to replace human interviewers.

It's to help candidates practice more effectively before the real interview.

What's Next?

I'm continuing to improve the platform with features such as:

Voice interviews
Coding interviews
Behavioral interview simulations
Company-specific interview preparation
Progress tracking over time
Personalized learning recommendations

Final Thoughts

Building this platform has been one of my favorite AI projects because it combines software engineering, machine learning, and user experience into a tool that solves a real problem.

If you're interested in AI-powered interview preparation, you can check out Talorr AI at https://talorr.com.

I also write about software engineering, AI, and startup development on my personal website: https://zakkasmi.com.

If you've built an AI product yourself, I'd love to hear what challenges you faced.

DEV Community: zaki kasmi