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.

The Problem: You Can't Practice Like It's Real

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:

• Ask me a coding problem (preferably one I hadn't seen)
• Let me talk through my approach out loud
• Interject with clarifying questions or constraints
• Give me a score and pointed feedback on my communication and solution

The obvious solution? Build it with an LLM.

What I Tried That Didn't Work

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.

The Approach: Prompt Engineering for an AI Interviewer

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:

1. Maintain a conversation context (role: system, assistant, user)
2. Inject a random problem from my curated list (or ask the AI to generate one)
3. Enforce that the AI never gives away the answer—only asks clarifying questions and gives hints after two mistakes
4. At the end, provide a structured evaluation

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):
        # 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:

• Context saving: I stored the full conversation so I could review it later.
• Problem bank: Instead of letting the AI generate problems (which sometimes produces weird or ambiguous ones), I curated a list of classic problems and had the AI pick one randomly.
• Time pressure: I used a simple timer in the CLI that beeped after 25 minutes—like a real interview slot.

What Eventually Worked

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.

Lessons Learned and Trade-offs

What worked well:

• Availability: I could practice anytime, anywhere. No scheduling.
• Repetition: I could repeat the same problem with different AI personas to solidify understanding.
• Focused feedback: The AI consistently evaluated the same dimensions (communication, correctness, etc.), which helped me track progress.

What didn't work:

• Depth of feedback: The AI sometimes missed subtle logical errors or gave overly generic feedback like "good job." I had to cross-check with actual solutions.
• Lack of visual presence: In real interviews, the interviewer's facial expressions and body language matter. AI can't replicate that.
• Cost: Running GPT-4 multiple times a day for 30-minute sessions added up (roughly $2-$3 per session). I switched to GPT-3.5-turbo for cheaper practice, but the feedback quality dropped.

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.

What I'd Do Differently Next Time

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.

Your Turn

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.