CareerMate: A Personalized RAG-Based Interview Preparation and Career Guidance System

Technical placement preparation requires students to juggle disconnected resources for Data Structures and Algorithms (DSA), SQL, resume review, company research, and communication practice, with no single system that understands a candidate's profile as a whole. This paper presents CareerMate, a full-stack, AI-driven platform that unifies resume analysis, skill-gap detection, topic-wise technical practice, case-study evaluation, communication coaching, multi-round mock interviews, and a Retrieval Augmented Generation (RAG) based knowledge assistant into a single personalized preparation workflow. The system analyses an uploaded resume against a chosen job role, generates a priority-ranked study plan from measured performance, and grounds its AI-generated answers in a curated knowledge base to avoid unreliable, free-form model output. This paper describes the system architecture, the preparation workflow, the core modules, and a working prototype of the system, along with the expected impact of a data-driven, single-platform approach to placement preparation over the fragmented, multi-tool status quo.