> 1. Does testing a candidate's ability to "steer" and debug AI-generated code make more sense to you than traditional algorithms?
Testing the candidate's ability to "steer" agents seems to be like testing their ability to know the Java API or to recite SOLID by heart.
> 2. How are you currently preventing these "prompt-only" developers from slipping through your own interview loops?
We don't ask anymore leetcode. We keep the usual systems design interview in which usage of AI is not needed (or at least we don't allow it because in this kind of interview we are more interested in seeing how the candidate thinks and so on)
We have a new stage in our job interview, though: generic Q/A about the fundamental of software engineering/computer science. Again, we don't care anymore how candidates produce code. We care about what they know, and what they don't know. What's the scope of their knowledge, and when do they need to rely on AI to come up with an answer. Silly (non-real) example: "Can you write a program that detects if another program halts?". The people we want are the ones who would say something about the Halting Problem but also perhaps be practical and perhaps ask more questions about such a program requirements.
You get the point: we look for people with a good breadth of knowledge, who can communicate well and know their shit. Whether they can use tool x or y (including LLMs), comes for granted for such people
Testing the candidate's ability to "steer" agents seems to be like testing their ability to know the Java API or to recite SOLID by heart.
> 2. How are you currently preventing these "prompt-only" developers from slipping through your own interview loops?
We don't ask anymore leetcode. We keep the usual systems design interview in which usage of AI is not needed (or at least we don't allow it because in this kind of interview we are more interested in seeing how the candidate thinks and so on)
We have a new stage in our job interview, though: generic Q/A about the fundamental of software engineering/computer science. Again, we don't care anymore how candidates produce code. We care about what they know, and what they don't know. What's the scope of their knowledge, and when do they need to rely on AI to come up with an answer. Silly (non-real) example: "Can you write a program that detects if another program halts?". The people we want are the ones who would say something about the Halting Problem but also perhaps be practical and perhaps ask more questions about such a program requirements.
You get the point: we look for people with a good breadth of knowledge, who can communicate well and know their shit. Whether they can use tool x or y (including LLMs), comes for granted for such people