Clarichat AI Interviewer

A full-stack application developed collaboratively within a team, designed to simulate real interview sessions by providing personalized feedback based on user responses to specific questions. The application features a dynamic dashboard for users to review past interviews and feedback across multiple job fields. Additionally, it incorporates testing suites, CI/CD pipelines, ensuring a robust and scalable development process.

Purpose and Direction

Clarichat was developed as part of a university studio subject aimed at enhancing our software development skills. The project was designed as a mock AI interviewer that provides users with personalized feedback based on their responses to specific questions.

Stack Explanation

For this project, we leveraged the OpenAI API to generate and deliver personalized feedback, which was then stored in a Firebase database and displayed on a dynamic dashboard. React was chosen as the front-end framework to ensure a scalable, responsive, and interactive user interface.

Issues and Lessons Learnt

One of the biggest challenges during development was team collaboration. While we had a clear vision of how Clarichat should function, defining the right approach to achieve our goals proved difficult. To address this, I took initiative multiple times to ensure all team members were aligned, informed, and working towards a unified direction.