Overview
AI Interview Coach is a side project that helps engineers prepare for technical interviews by simulating realistic interview scenarios. The platform covers system design discussions, coding problems with follow-up questions, and behavioral interviews — providing structured feedback after each session.
Problem
Interview preparation is often unstructured. Engineers read system design books and solve LeetCode problems in isolation, but real interviews involve dynamic conversations, follow-up questions, and time pressure. Most mock interview platforms are either too expensive or too generic to provide meaningful practice.
Architecture
- Frontend: Next.js App Router with real-time chat-style interface
- Interview Engine: OpenAI API with carefully crafted system prompts that simulate different interviewer personas (senior engineer, hiring manager, system design expert)
- Session Management: Each interview session maintains context across multiple exchanges
- Feedback Generator: Post-interview analysis evaluates communication clarity, technical depth, and areas for improvement
- Progress Tracker: PostgreSQL stores session history with tagged strengths and weaknesses
Tradeoffs
- LLM-based over rule-based: Using GPT-4 provides natural conversation flow but introduces latency and cost per session
- Streaming responses: Server-sent events for real-time token streaming creates a natural interview feel
- No code execution: System design interviews are conversation-only; coding assessment relies on the AI's ability to evaluate pseudocode
Implementation
- Custom prompt engineering for three interview types with calibrated difficulty levels
- Session state management using React context with server-side persistence
- Rate limiting to control API costs (5 sessions per day on free tier)
- Markdown rendering for code snippets and architecture discussions
Challenges
- Prompt engineering for consistent, realistic interviewer behavior across sessions
- Handling edge cases where the AI agrees with incorrect answers instead of pushing back
- Balancing response quality with latency — longer prompts produce better feedback but slower responses
Future Improvements
- Voice-based interviews using speech-to-text and text-to-speech
- Peer-to-peer mock interview matching
- Company-specific interview preparation paths
- Integration with LeetCode for coding round practice