Day 10 of Becoming an AI Engineer | Building an Adaptive AI Interviewer Agent with GPT-4.1 Streamlit
Today I built an AI Engineer Interviewer Bot using GPT-4.1, LangGraph, Streamlit, Whisper, and OpenAI TTS.
This project simulates a real technical AI interview experience where the system dynamically adapts based on your responses.
The interviewer can:
Ask technical interview questions using OpenAI Text-to-Speech
Listen to spoken answers using Whisper speech recognition
Evaluate technical depth using structured AI scoring
Dynamically increase or decrease question difficulty
Generate a complete hiring report and improvement roadmap
One of the most interesting parts of this project was understanding how conversational AI systems maintain state and adapt in real time.
Instead of a fixed chatbot flow, the system behaves more like an intelligent interviewer:
→ listening
→ evaluating
→ reasoning
→ adjusting
→ generating feedback
Tech Stack Used:
GPT-4.1
LangGraph
Streamlit
OpenAI TTS
Whisper
AI Agents
Python
This project helped me better understand:
Real-time conversational AI
Adaptive agent workflows
Voice-based AI interfaces
Structured evaluation systems
LangGraph state management
AI interview simulation systems
GitHub Repository: - https://github.com/prodcodebyloki/ai_...
If you're learning AI Engineering, AI Agents, LangGraph, RAG, LLM applications, or building GenAI systems, this project is a great example of how modern AI workflows can move beyond simple chatbots into adaptive intelligent systems.
Day 11 next 🚀