“To provide students and engineers with realistic, low-pressure interview practice that mirrors actual technical and behavioral screening loops, emphasizing vocal articulation, technical depth, and handling unexpected follow-ups.”
— AI Interviewer Architecture Brief
AI Interviewer is an intelligent interview preparation platform that replaces static question-and-answer flashcards with a dynamic, conversational simulation. Candidates experience interactive technical and behavioral interview sessions where questions adapt based on prior answers, and performance is evaluated across technical precision and communication clarity.
Traditional interview preparation tools rely on static question lists and generic multiple-choice quizzes. They fail to test candidates in realistic verbal conditions, offer no dynamic follow-up questioning when an answer is incomplete, and provide superficial, keyword-matching feedback that does not assess conceptual understanding.
Integrated a real-time speech ingestion pipeline in Python with Generative AI prompt orchestration, analyzing spoken responses against technical rubrics and generating tailored follow-up inquiries.
Candidate selects role domain (Software Engineering, Data Science, HR) and experience level.
System presents technical scenario; candidate speaks response via microphone.
Audio is transcribed and preprocessed for evaluation.
LLM evaluates technical accuracy and generates contextual follow-up questions.
Session concludes with a comprehensive multi-attribute evaluation report.
Speech Recognition Ingestion Pipeline
Integrated real-time audio capture and transcription in Python, handling background noise filtering and sentence boundary detection for conversational flow.
Dynamic Question Adaptation Engine
Engineered structured prompt chains that assess candidate answer depth. If a candidate gives a high-level answer, the model probes deeper into implementation trade-offs; if they struggle, it provides progressive hints.
Requiring the model to return typed JSON scorecards enabled programmatic UI rendering and historical progress tracking, avoiding unstructured prose responses.
Enabled consistent rendering of candidate metrics, radar charts, and categorized feedback points.
Combining evaluation and question generation in a single prompt caused the model to favor polite praise over rigorous technical assessment.
Decoupled the evaluation step from conversational interaction, ensuring objective and critical scoring.