I just recently helped a student walk the entire Meta AI Coding interview, to be honest, this round is completely different from the traditional LeetCode interview. Many people are still frantically solving questions, but the core of this round is actually: can you "use AI + control AI", not whether you can write the optimal solution by hand.
Interview format
The entire process takes approximately 60 minutes. A big question is usually divided into 2-3 subtasks and completed in the CoderPad environment. The more critical point is that AI is allowed to be used in this round. It sounds like the difficulty has been reduced, but the reality is that the difficulty has directly changed direction.
Review of specific topics
Fix valid_recommend function
The essence of this question is debug + complementary logic.
Gave one Valid_recommend 函数，让它通过已有 test case。面试官直接说这题不建议用 AI，其实也确实没必要。
I did a quick scan:
- The input is a user + user list
- 但函数里没有判断 list 里是否包含 user 自己
So there will be situations where you recommend yourself.
Here I directly added a simple judgment logic. It was fixed in about two lines of code and passed smoothly.
What this question is actually looking at is:
- Can you read code quickly?
- Can the bug be located?
- Will it be over-engineer?
Implement random_recommend
需要实现一个 random_recommend function。
At the beginning, I also made a typical mistake. I directly let AI generate the complete code. As a result, problems immediately occurred when I pasted it in and ran it.
To adjust the strategy later, first think through the overall logic yourself, and then let AI assist you in writing part of the code, such as random selection or structural parts. I went back and forth with AI for several rounds, gradually correcting the logic, and finally ran through it. The key point of this question is that you cannot completely trust AI, but you must have the ability to judge and correct it.
Evaluate the effectiveness of recommendation algorithms
The third question is an open question: how to measure the effectiveness of the friend recommendation algorithm.
I initially asked AI to give some common indicators, such as precision, recall, click-through rate, etc., but the interviewer quickly reminded me to combine it with the current data structure. The User class in this question actually only has id and currentFriends, and no more user attributes, so many conventional recommendation indicators cannot be implemented.
Later, I converged my ideas on existing data, such as mutual friends (number of mutual friends), whether a connection is formed after recommendation, etc. The essence of this question is to test whether you have awareness of data constraints, not whether you can memorize indicators.
OA / What to do if you get stuck in the interview
The problem that many students have now is actually not with algorithms, but with this new interview format. For example, they don’t know when to use AI, they don’t know how to ask AI, or AI gives code but doesn’t know whether it’s right or wrong.
AI Coding interviews like this can actually be improved quickly through targeted training. Our Programhelp team has taken many similar cases recently, mainly doing real-life process mocks, AI usage training, and VO Battle Companion . Many students only need to go through the complete process once or twice, and their overall performance will be much more stable.
