On the post-grad job hunt right now - I note that most employers will ask in a technical interview or whiteboard interview "how are you using LLMs?"

It's tough to answer because you want to hedge for both an AI enthused employer and an AI hesitant employer with limited information about who they are and how they personally use these products. I've been responding with a sort of long winded answer about how 'there is clearly a learning curve for how this technology fits into any process and how I always always always double double double check yadayadayada'

I'm probably using the chat/ask functionality on a daily basis for quick debugging / new technology learning questions but I have yet to really use the fully agent or computer-use products because I've had more bad results than good the few times I've tried them (re-factoring a big repo of decades old fortran+C code for modern compiler/OS some things started to work but ultimately I abandoned that effort).

> It's tough to answer because you want to hedge for both an AI enthused employer and an AI hesitant employer with limited information about who they are and how they personally use these products.

Have you considered just answering truthfully?

Would you even want to work somewhere where you need to play a role and where they flip out when you say the wrong word you should've correctly guessed through mind reading? That sounds not like a job but a toxic relationship.

I think what would be great is to have eg a concise example where it works well for you and a concise example where it doesn’t. This shows you have explored it and thought about it enough to explain interesting observations. It’s good to then be ready to go deeper if of interest.

> It's tough to answer because you want to hedge for both an AI enthused employer and an AI hesitant employer with limited information about who they are and how they personally use these products.

I'm an old hat on both sides of this type of discussion from a post-grad view.

Recommendation: use it to own the conversation and to signal mutual fit. Yes, your idea of AI lover versus hesitant matters. I recommend reframing the question to pivot to your fit to the org (and org fit to you) question. Show/concisely explain how you consider whether LLMs are fit to a task and how to tell it improves outcomes.

An outcome focus and willingness to show thought process around a common use case will be a substantially strong response.

Replying as a hiring manager since this might help other post-grad job seekers:

- Any long-winded answer to a question is immediate out and has been for years.

- Not having used agents and not being able to comment on what to do and what not to do with them is immediate out since early this year.

still 10x better than the 'finish this leetcode tweak algorithm in 20 minutes and tell me your thought process along the way, and yes you will never need that skill in the real job but we need find out who had time to cram for the algorithm books in the last few months'

I understand the pressure to get employed from your perspective, but differences in opinion should be voiced out and typically aren't the thing leading to rejection from the company. It's common that engineering leads seek out people with different backgrounds and views to work on the same team. If anything, answering truthfully will make you stand out from others who've responded in a generic, heavily hedged way.

> re-factoring a big repo of decades old fortran+C cod

Having been in academia in the past and now in software I can say with a lot of certainty that this will take a lot more upfront work than otherwise.

Academic code does not have a lot of structure. And usually lacks a lot in terms of tests. While AI is best when it can mimic patterns as well as there are tests to target.

So you will probably need to budget a few weeks to establish good patters, docs as well as testing patterns before you can seriously make it really do what you want it to do.

You should find out during the screener what kinds of view the executives have on LLMs. don’t wait until you’re midway through the third round.

I am also on the job market, but as a Senior. Pro-tip: ask them this question before they ask you. “One quick question I have about the company culture, …”

A balanced answer that’s often true these days is: you’ve found that LLMs are impressively useful in some cases but fall dramatically short in others.

They might not have an answer in mind. They might just be exploring how you adapt to new tools and methods.

Consider using agent mode for some things, you are definitely missing out.

The analogy I've had for myself is that it feels like using a bulldozer to dig rather than a shovel. If you use it to dig archaeological artifacts, it can make things worse than you started. A lot of the work however, is just moving dirt around, so you are wasting time by using a shovel.

> It's tough to answer because you want to hedge

You should just be honest. If you're not a good fit for the company then you should honestly be eager to discover this.

> I've been responding with a sort of long winded answer

"I don't. I personally don't find value in them for the type of work I do. I am also uncomfortable with using their outputs under the current copyright regime. I also question how competitive any organization can possibly be if LLMs become the main driver of their work products."

> I've had more bad results than good the few times I've tried them

"I prefer to write correct code rather than debug bad code generated from a limited context window."

Exact same experience. My background is embedded and VLSI so I hedge my bets by saying that LLM are ok for Python scripting, but not there yet for synthesizable Verilog. It is really hard to see if the "how are you using LLMs?" question is for "we are AI Native™" or a form of cheating (like in university).

The reason you've had more bad results than good is because you haven't fully learned how to use LLMs yet. They are not as simple as they first appear. I think a lot of people think using a coding agent is just a case of firing it up and telling it what to do and expecting to get it right first time. When it doesn't they just think it's no good and like you abandon the effort.

The reason a technical interviewer will be asking this question is because they want to see how you adapt to using new technologies, LLMs being one of the most disruptive technology that has hit the tech industry since at least the internet. You will likely be expected to use LLMs and they will want to know that you are someone who truly understands the capabilities of them - upsides and downsides, where to use them, what guardrails you need to put in place.

I'd encourage you to revisit the re-factoring task you worked on. Work out why it didn't work, work out what didn't work about it and if you have the chance try again, but use different techniques, there's a lot of conversations going on about what people find working and not working - try to join that conversation. Try to document what you learn. Then in the interview discuss these rather than just saying you gave up. The interviewer isn't going to check up on how successful your project was, they just want to know how you think and how you approach problems.

One trick is to ask them that question first to gauge their perspective on it first

Just answer honestly, and include a note that you intend to fully comply with the companies AI policies. Thats the best answer anyone can give.

> It's tough to answer because you want to hedge for both an AI enthused employer and an AI hesitant employer

That this doesn't have a clear and obvious answer one can expect shows how the issue is politics, not strategy.

When you apply as a mechanic, there is no such weird political debates about certain power tools where people have passionate opinions on which tool to use.

I personally think "I pretty much use it as a faster and more flexible StackOverflow" is probably the most neutral position you can have on it

That's probably not going to be enough for AI maxxers, but it probably won't be too much of a turn off for anyone but the most extreme AI minners, and everyone in between will probably be fine with it.

Frankly I plan to steer well clear of any "the majority of our code is AI generated" shops for the foreseeable future. Seems like disasters waiting to happen and I'd rather let other people step on those rakes

"for entertainment value, when i'd like to see how an enthusiastic 5-year-old would react to the task."