Let me tell you about the worst 45 minutes of my job search. I'm on a video call with a FAANG company. The interviewer shares a LeetCode medium problem. My brain goes completely blank. I know I've solved similar problems before, but in that moment? Nothing. Just me, sweating, typing random stuff while making excuses.

That was interview fail #1. Fails #2 and #3 weren't much better.

Then I started using AI tools during my prep. And yeah, eventually during actual interviews too. Here's what I learned about using AI for coding interviews - the good, the bad, and the stuff that actually matters.

The Problem with Coding Interviews

Here's the thing nobody talks about: coding interviews are not like real programming. In real life, you Google stuff. You read documentation. You take breaks. In interviews? You have 45 minutes, someone watching you, and you're supposed to solve a puzzle you've never seen while explaining your thought process out loud.

It's a performance, not actual engineering. And some of us just aren't great performers under pressure.

What AI Coding Assistants Actually Do

Let's be real about what these tools are and aren't:

Tools I Actually Tested

LastRound AI

This is what I use now. The coding support is solid - it can see your screen (when you let it) and suggest approaches. The key thing for me: it's invisible during screen share. When my interviewer asks me to share screen for a CodeSignal assessment, they don't see the AI. That matters.

Their free tier gives you 15 credits monthly which is enough for actual interviews. I use Google for practice, save the AI for when it counts.

LockedIn AI

Their coding copilot has screen analysis and gives optimization tips. Response time is fast. The dual-layer system takes getting used to though. More features but steeper learning curve.

Free tier is 10 credits + 10 minutes daily. Usable but limiting.

Parakeet AI

Can capture LeetCode-style questions from screen share. One-time payment model ($29.50 for 3 interviews). No free tier though, so harder to test before committing.

The Screen Share Problem

This is the big one. Most coding interviews require screen sharing. If your AI assistant shows up on the screen share, you're done. Not just for that interview - probably blacklisted from the company.

Here's what I learned:

How I Actually Use It Now

My current approach for coding interviews:

1. Before the interview: Grind LeetCode the old fashioned way. AI won't save you if you don't understand the patterns. I do 2-3 problems a day.
2. Problem appears: I read it. Think for 30 seconds. Start explaining my thought process out loud. The AI is there but I try to solve it first.
3. When I'm stuck: Glance at the AI suggestion. Usually it's a nudge like "consider using a hashmap" not full code. That's actually what you want.
4. Syntax brain freeze: This is where AI shines. Is it arr.length or arr.size()? AI tells me instantly instead of me blanking for 30 seconds.
5. Edge cases: AI often catches stuff I forget under pressure. Empty array? Negative numbers? Good reminders.

The Ethics Question

Look, I know some people have strong opinions about this. Here's my take: coding interviews are already a flawed way to evaluate engineers. They test performance under artificial pressure, not actual job skills.

Using an AI to help with syntax and hints? It's not that different from having a good memory. The AI isn't going to help me design systems or debug production issues. It just helps me get past a gate that arguably shouldn't exist in its current form.

That said - if you can't explain your code and answer follow-up questions, you'll still fail. AI is a crutch, not a replacement for understanding.

What Made the Difference for Me

After fail #3, I changed my approach:

• 80% grinding, 20% AI - The practice still matters most. AI is for the interview day nerves.
• Mock interviews with the tool - Getting used to having AI suggestions without being distracted by them.
• Focus on explanation - AI helps with code but I still need to explain everything clearly. Practice talking through problems.
• Know when NOT to look - Sometimes the AI suggestion is overkill. Simple problems don't need fancy solutions.

Results

Since changing my approach: 5 coding interviews, 3 passes, 1 offer accepted.

Was it all because of AI? No. The grinding mattered. The mock interviews mattered. But having that safety net when my brain froze up? That helped too.

Whatever tools you use, put in the actual practice. There are no shortcuts to understanding algorithms. But there are tools that help you perform better when it counts.

Last updated: January 2025