How I Used AI to Simulate Realistic Coding Interviews

A backend developer built an AI-powered coding interview simulator using OpenAI's GPT-4 to practice for senior role interviews. The system uses a structured system prompt to enforce strict interviewer behavior, providing problems, follow-up questions, and evaluations. The developer found that existing platforms like Pramp and interviewing.io had scheduling and consistency issues, leading to the creation of this on-demand practice tool.

I've been a backend developer for about six years, and I thought I had the interview game figured out. Then I applied for a senior role at a FAANG-adjacent company, and the first round of live coding hit me like a truck. I froze. I could solve LeetCode mediums in my sleep, but under the gun with a stranger watching? My brain turned to static. After bombing that interview, I decided I needed more than just dry practice. I needed a way to simulate the pressure, the unexpected follow-ups, and the weird silences. I tried peer mock interviews, but scheduling was a nightmare, and feedback often missed the small things—like how I talk through my thought process, or whether I jump to code too quickly. That's when I turned to AI. Not to replace human interviewers—but to build a dedicated, on-demand practice partner. Here's how I did it, the code I used, and the trade-offs I discovered. I had been preparing for weeks. I could recite Big O notation in my sleep. But I realized that most of my practice was static: I'd look at a problem, think it through, maybe scribble some pseudocode, then check the solution. That's not an interview. An interview is a live, interactive conversation. You need to explain your reasoning, handle interruptions, and occasionally correct course when the interviewer drops a hint. I needed a tool that would: The obvious solution? Build it with an LLM. First, I tried using ChatGPT in a generic chat. I'd say, "Ask me a medium-difficulty coding problem about trees." It worked—once. But the conversation was too loose. The AI would often forget it was an interviewer, start giving hints, or go off topic. I needed a more structured setup. I also tried a few existing platforms like Pramp and interviewing.io, but they rely on human peers or live engineers. Scheduling conflicts and varying skill levels made consistent practice hard. Plus, I wanted to practice at 2 AM after the kids were asleep. Instead of a generic chat, I used a system prompt to define the AI's role as a strict technical interviewer. I built a simple Python script that would: Here's the core of the code I used. It leverages the OpenAI API, but you could adapt it to any LLM. python import openai import json openai.api key = "your-api-key" class AIInterviewer: def init self, topic="arrays", difficulty="medium" : self.system prompt = f"""You are a senior software engineer conducting a technical interview. The candidate is interviewing for a senior backend role. Focus on {topic} problems at {difficulty} difficulty. Rules: - First, present a single coding problem. Do NOT provide any solution. - Let the candidate talk through their approach. - Ask follow-up questions to clarify their reasoning e.g., 'What is the time complexity?', 'Have you considered edge cases?' - If the candidate makes a mistake, give one hint. If they still can't solve it, move on. - At the end, provide a structured evaluation with scores 1-10 for: problem understanding, communication, technical correctness, and overall. - Never reveal the answer unless explicitly asked after the evaluation.""" self.messages = {"role": "system", "content": self.system prompt} def ask problem self : First prompt: get the problem from the AI initial prompt = "Start the interview. Ask me a coding problem." self.messages.append {"role": "user", "content": initial prompt} response = openai.ChatCompletion.create model="gpt-4", messages=self.messages assistant msg = response.choices 0 .message.content self.messages.append {"role": "assistant", "content": assistant msg} return assistant msg def respond self, user input : self.messages.append {"role": "user", "content": user input} response = openai.ChatCompletion.create model="gpt-4", messages=self.messages assistant msg = response.choices 0 .message.content self.messages.append {"role": "assistant", "content": assistant msg} return assistant msg Usage interviewer = AIInterviewer topic="trees", difficulty="medium" print interviewer.ask problem Then in a loop: reply = input "Your answer: " print interviewer.respond reply This is a minimal version. In practice, I added a few enhancements: The key wasn't the AI itself but the structured interaction flow. The system prompt forced the AI to stay in character. I practiced for two weeks, about 30 minutes a day. The improvement was real. I stopped freezing because I'd already faced a dozen AI-generated interviewers with different quirks. The feedback at the end—especially on communication—helped me slow down and be more deliberate. I also added a variant where the AI would simulate a grumpy interviewer who interrupts. That was brutal but effective. What worked well: What didn't work: When not to use this approach: If you're early in your preparation and don't know basic data structures yet, an AI interviewer may be overwhelming. Start with LeetCode and understand fundamentals first. If I were to rebuild this, I'd incorporate multi-agent simulations—one AI as the interviewer, another as a silent observer that provides a detailed breakdown after the session. That could catch more nuance. I'd also open-source it with a simple UI, but for now, the Python script works. I looked into some existing tools that abstract this pipeline. For example, AI Interwest https://ai.interwestinfo.com/ offers a ready-made interview simulation platform, but I enjoyed building my own to understand the prompt engineering deeply. Interviews are stressful, but practicing in a realistic environment can make all the difference. Whether you roll your own AI coach or use a turnkey solution, the key is to simulate the conversation, not just the algorithm. What's your favorite way to practice for coding interviews? Have you tried AI-based methods? I'd love to hear what worked or didn't for you.