AI Interview Assistant - RAG
Built a full-stack AI Interview Assistant using RAG architecture, vector embeddings, and LLMs to conduct personalized mock interviews, analyze resumes, and generate context-aware interview responses through a modern interactive dashboard.
Category:
AI & ML
My Role:
AIML Engineer
An AI-powered interview preparation platform built using Retrieval-Augmented Generation (RAG) architecture to simulate realistic mock interviews, analyze resumes, and provide intelligent, context-aware interview assistance. The platform allows users to upload resumes and interview preparation materials, which are processed into vector embeddings and stored in a vector database for semantic retrieval during conversations.
The system combines a modern full-stack architecture with FastAPI, Next.js, LangChain, ChromaDB, and Large Language Models to deliver personalized interview experiences, technical question generation, resume-aware responses, and AI-driven feedback through a premium interactive UI inspired by modern SaaS applications.
Approach in this project
I started with the idea of building an interview preparation platform that feels closer to a real product experience rather than a simple AI chatbot. I first focused on planning the overall user flow — from resume upload and interview setup to AI-based conversations and feedback. One of the main goals was not only to help users practice interviews, but also to help them understand the kind of questions they might realistically face based on their resume, skills, and target role.
The frontend was built using Next.js, TypeScript, and Tailwind CSS, with emphasis on creating a clean and modern interface that feels interactive, responsive, and easy to navigate. A warm visual design with dashboard layouts, reusable components, and subtle animations was used to give the platform a more polished and engaging experience.For the backend, I used FastAPI to manage APIs, resume uploads, and communication between the frontend and AI services. I then built a RAG-based pipeline using LangChain, ChromaDB, and sentence-transformer embeddings to make the assistant context-aware. Uploaded resumes and preparation documents were processed by extracting text, splitting content into chunks, generating embeddings, and storing them inside a vector database for semantic search. This allowed the system to retrieve relevant information during conversations and generate more personalized interview questions, responses, and feedback using Large Language Models.
The final result was a fully functional AI Interview Assistant capable of conducting personalized mock interviews, analyzing resumes, retrieving contextual information through vector search, and generating intelligent AI-driven interview interactions in real time. The platform successfully combined modern frontend engineering, backend API development, and GenAI-based RAG architecture into a cohesive full-stack application.
The project demonstrates practical implementation of real-world AI engineering concepts including vector databases, semantic search, prompt engineering, document ingestion pipelines, and LLM integration while also showcasing strong UI/UX design and full-stack development capabilities.
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