Landing interviews is difficult. Passing them is even harder.
Over the past year, I noticed the same pattern among developers and job seekers:
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.
Traditional interview preparation has several limitations:
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 platform should be able to:
Instead of memorizing hundreds of questions, candidates practice the questions they're actually likely to receive.
I wanted something scalable and easy to iterate on.
The backend exposes APIs for interview generation, scoring, feedback, and progress tracking.
The first challenge was building context.
Rather than asking random questions, the AI first understands:
This context becomes the foundation for every interview session.
Every job description is different.
The AI extracts:
This allows interview questions to closely match the actual position.
Instead of storing thousands of hard-coded questions, the platform generates them on demand.
For example, a backend engineer might receive questions about: A frontend developer would instead receive questions about:
Each interview becomes unique.
The most challenging part wasn't generating questions.
It was evaluating answers fairly.
The AI scores answers based on multiple factors:
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.
At the end of an interview, candidates receive a detailed report covering:
This transforms interview preparation into a continuous learning process rather than a one-time assessment.
Building AI products taught me several important lessons.
Large language models perform dramatically better when given structured, relevant context.
Small prompt changes often produced significant improvements in response quality.
People don't just want to know how they performed.
They want to know how to improve.
The goal isn't to replace human interviewers.
It's to help candidates practice more effectively before the real interview.
I'm continuing to improve the platform with features such as:
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.