How I Used AI to Simulate Realistic Coding Interviews

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.

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):
        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

interviewer = AIInterviewer(topic="trees", difficulty="medium")
print(interviewer.ask_problem())
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 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.