I recently wrote a much more detailed breakdown of how I prepare for Google Meet technical interviews, along with my observations about modern interview workflows and AI-assisted preparation. If you'd like the full version with screenshots and additional examples, you can check out the original article on the Linkjob AI blog “AI for Google Meet interviews" before continuing.
When people hear "Google Meet interview," they usually picture a normal video call. That was exactly my assumption before I started preparing for several remote technical interviews earlier this year.
After going through multiple interview rounds and comparing Google Meet with coding platforms like Coderbyte, HackerRank, and Codility, I realized they're solving very different problems. A coding assessment platform evaluates how you solve programming challenges inside a dedicated environment, while Google Meet is simply the communication layer. The actual interview experience depends almost entirely on how the company chooses to structure it.
That realization completely changed how I prepare for remote interviews.
Google Meet Isn't Really an Interview Platform
One misconception I had at the beginning was assuming Google Meet worked like an online assessment platform.
It doesn't.
Google Meet itself is designed for meetings, not technical evaluations. Whether you're asked to share your screen, solve coding problems, explain system design, or simply answer behavioral questions depends on the interviewer—not the platform itself.
Once I understood that distinction, I stopped treating every remote interview the same way.
Instead of preparing for "Google Meet," I started preparing for the interview format that happened to use Google Meet.
That sounds like a small difference, but it made my preparation much more focused.
What Actually Changed My Preparation
Earlier in my career, I thought interview preparation meant solving as many LeetCode problems as possible.
Now my routine looks very different.
I still practice algorithms, but I spend almost as much time explaining my thought process out loud as I do writing code. During mock interviews, I deliberately pause to explain trade-offs, discuss edge cases, and describe why I choose one solution over another.
Surprisingly, those communication exercises have improved my confidence more than solving another hundred programming questions.
Interviewers aren't just evaluating code—they're trying to understand how you think.
Where AI Has Been Most Helpful
AI has become part of my preparation workflow, but probably not in the way many people imagine.
I don't use it to replace learning.
I use it to accelerate feedback.
For example, after finishing a coding problem, I'll ask AI to compare my solution with alternative approaches or point out edge cases I overlooked. Sometimes I'll practice explaining the same algorithm several different ways just to make sure I actually understand it instead of memorizing it.
That feedback loop is incredibly valuable because it turns one practice problem into several different learning opportunities.
Why I've Continued Using Linkjob AI
I've experimented with several AI interview assistants over the past few months, and each one approaches interview preparation a little differently.
What kept me using Linkjob AI wasn't that it magically answered every question. It was how naturally it fit into my existing workflow.
Before an interview, I usually upload the job description, review the technologies mentioned in the role, and run a few mock interview sessions. Instead of giving generic explanations, I can practice responses that are much closer to the kind of conversations I'd expect during a real technical interview.
For coding interviews, I also like using it to review different solution strategies after I've already attempted the problem myself. That helps me understand why one implementation might be preferable over another instead of simply memorizing a final answer.
For me, that's where AI becomes genuinely useful—as a learning companion rather than a replacement for preparation.
Communication Matters More Than Perfect Code
One lesson I've learned after several Google Meet interviews is that communication often has a bigger impact than candidates expect.
I've seen people write solid code but struggle to explain their reasoning.
I've also seen candidates with imperfect solutions leave a great impression because they communicated clearly, discussed trade-offs, and responded thoughtfully to follow-up questions.
That's one reason I now spend so much time practicing explanations instead of only practicing implementation.
Being able to describe why you made a decision is often just as important as the decision itself.
My Current Practice Routine
My interview preparation has become surprisingly consistent over time.
A typical practice session looks something like this:
- Read the job description and identify the core technical topics.
- Solve one or two coding problems without assistance.
- Review alternative approaches with AI.
- Practice explaining the solution aloud as if an interviewer were asking follow-up questions.
- Reflect on what felt unclear and repeat the process with another problem.
It's a simple routine, but repeating it consistently has made me much more comfortable during live interviews.
AI Doesn't Replace Preparation
One interesting thing I've noticed from developer communities is that opinions about AI in interviews vary wildly.
Some engineers refuse to use AI at all.
Others expect it to solve every problem for them.
Personally, I think both extremes miss the point.
The biggest value comes from using AI between interviews rather than relying on it during interviews. It can shorten learning cycles, expose weak areas, and help you practice technical communication much more efficiently than studying alone.
But you still need to understand your own code.
You still need to explain your decisions.
And you still need to demonstrate genuine engineering judgment when the interviewer asks unexpected follow-up questions. Discussions across developer communities increasingly emphasize that interviewers are paying close attention to candidates' reasoning and technical fundamentals rather than just polished answers.
Final Thoughts
Looking back, researching Google Meet interviews taught me something much more useful than I expected.
The platform itself isn't really the difficult part.
The real challenge is adapting to modern technical interviews, where communication, structured thinking, and continuous learning matter just as much as writing correct code.
AI has definitely become part of my preparation process, but not because it replaces practice. It helps me learn faster, identify weaknesses earlier, and build confidence before I walk into the actual interview.
And honestly, that's probably the biggest takeaway from this whole experience. The best interview preparation isn't about finding shortcuts—it's about creating a learning workflow that helps you improve every single week.
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