Table of Contents
- Overview
- Role
- Problem
- Goal
- Solution
- Technical Implementation
- Challenges and Learnings
- Final Thoughts
Overview
Pocket Interview is an AI-powered iOS application that provides realistic mock interview experiences. Built with SwiftUI and Tavus API, the app features two distinct AI interviewers specializing in technical and behavioral interviews, offering personalized practice sessions that adapt to user performance.
👨💻 Role
iOS Engineer
❓ Problem
Job seekers face significant challenges preparing for interviews:
- Limited access to realistic practice opportunities with feedback.
- Difficulty practicing technical interviews without peer availability.
- Lack of personalized feedback on communication and behavioral responses.
- Interview anxiety from insufficient preparation.
🎯 Goal
- Create affordable, accessible interview practice available anytime.
- Implement AI interviewers with distinct personalities for technical and behavioral sessions.
- Provide actionable feedback on responses, body language, and communication.
- Build confidence through progressive difficulty and personalized recommendations.
✨ Solution
Core Features
Dual AI Interviewers:
- Tech Interviewer: Focuses on coding problems, system design, and technical knowledge
- Behavioral Interviewer: Specializes in STAR method responses and soft skills
Adaptive Interview Sessions:
- Industry-specific question (Tech, Finance, Healthcare, etc.)
- Custom interview length (15, 30, 45 minutes)
Comprehensive Feedback:
- Response quality analysis
- Communication pattern insights
- Progress tracking across sessions
🛠️ Technical Implementation
SwiftUI Architecture
- MVC Pattern: Clean separation
- Combine Framework: Reactive state management
- Swift Concurrency: Async/await for API communication
Tavus API Integration
- Real-time Video Generation: AI interviewer with natural facial expressions
- Natural Language Processing: Evaluate response quality and relevance
- Adaptive Questioning: Dynamic question selection based on performance
⚙️ Challenges and Learnings
- AI Response Quality: Extensive prompt engineering needed to create natural interviewer personalities and constructive feedback.
- Real-time Performance: Minimizing AI response latency critical for maintaining natural conversation flow.
- Privacy Concerns: Implemented clear data policies and on-device processing to build user trust.
✨ Final Thoughts
- Judgment-Free Practice: AI interviewer provided safe environment for repeated practice without human pressure.
- Personalization Matters: Adaptive difficulty and industry-specific content significantly improved user engagement.