How I use LLMs as a staff engineer
143 comments
·February 4, 2025toprerules
satellite2
I just don't fully understand this position at this level. Personally I know exactly what the next 5 lines need to be, and whether I write them or auto complete or some AI write them doesn't matter. I'll only accept what I had in mind exactly. And with Copilot for boilerplate and relatively trivial tasks that happens pretty often. I feel I'm just saving time / old age joint pain.
tokioyoyo
I will get probably heavily crucified for this, but to people who are ideologically opposing AI generated code — executives, directors and managerial staff think the opposite. Being very anti-LLM code instead of trying to understand how it can improve the speed might be detrimental for your career.
Personally, I’m on the fence. But having conversations with others, and some requests from execs to implement different AI utils into our processes… making me to be on the safer side of job security, rather than dismiss it and be adamant against it.
feoren
> executives, directors and managerial staff think the opposite
Executives, directors, and managerial staff have had their heads up their own asses since the dawn of civilization. Riding the waves of terrible executive decisions is unfortunately part of professional life. Executives like the idea of LLMs because it means they can lay you off; they're not going to care about your opinion on it one way or another.
> Being very anti-LLM code instead of trying to understand how it can improve the speed might be detrimental for your career.
You're making the assumption that LLMs can improve your speed. That's the very assumption being questioned by GP. Heaps of low-quality code do not improve development speed.
simonw
I'm willing to stake my reputation on the idea that yes, LLMs can improve your speed. You have to learn how to use them effectively and responsibly but the productivity boosts they can give you once you figure that out are very real.
Aeolun
> Executives like the idea of LLMs because it means they can lay you off; they're not going to care about your opinion on it one way or another.
Probably, but when the time comes for layoffs the ones that will be the first to go are those that are hiding under a rock, claiming that there is no value to those LLM’s even as they’re being replaced.
subw00f
I think I’m having the same experience as you. I’ve heard multiple times from execs in my company that “software” will have less value and that, in a few years, there won’t be as many developer jobs.
Don’t get me wrong—I’ve seen productivity gains both in LLMs explaining code/ideation and in actual implementation, and I use them regularly in my workflow now. I quite like it. But these people are itching to eliminate the cost of maintaining a dev team, and it shows in the level of wishful thinking they display. They write a snake game one day using ChatGPT, and the next, they’re telling you that you might be too slow—despite a string of record-breaking quarters driven by successful product iterations.
I really don’t want to be a naysayer here, but it’s pretty demoralizing when these are the same people who decide your compensation and overall employment status.
adamredwoods
>> But these people are itching to eliminate the cost of maintaining a dev team, and it shows in the level of wishful thinking they display.
And this is the promise of AI, to eliminate jobs. If CEOs invest heavily in this, they won't back down because no one wants to be wrong.
I understand some people try to claim AI might make net more jobs (someday), but I just don't think that is what CEOs are going for.
mostertoaster
I think with LLMs we will actually see the demand for software developers who can understand code and know how to use the tools sky rocket. There will be ultimately be way more money in total going towards software developers, but average pay will be well above the median pay.
chefandy
Just another way for the people on top to siphon money from everyone else. No individual contributor is going to be rewarded for any productivity increase beyond what is absolutely required to get them to fulfill the company’s goals, and the goal posts will be moving so fast that keeping up will be a full time job. As we see from the current job market, the supply of problems the commercial software market needs more coders to solve maybe isn’t quite as bountiful as we thought it was, and maybe we won’t need to perpetually ramp up the number of developers humanity has… maybe we even have too many already? If a company’s top developer can do the work of the 10 developers below them, their boss is going to happily fire the extra developers— not think of all the incredible other things they could use those developers for. A lot of developers assume that one uber-productive developer left standing will be more valuable to the company than they were before— but now that developer is competing with 10 people that also know the code base willing to work for a lot cheaper. We get paid based on what the market will bear, not the amount of value we deliver, so 100% of that newfound profit goes to the top and the rest of it goes to reducing the price of the product to stay competitive with every other company doing the exact same thing.
Maybe I’m being overly cynical, but assuming this isn’t a race to the bottom and people will get rich being super productive ai-enhanced code monsters, to me, looks like a conceited white collar version of the hustle porn guys that think if they simultaneously work the right combo of gig apps at the right time of day in the right spots then they can work their way up to being wealthy entrepreneurs. Good luck.
rectang
This has been true for every heavily marketed development aid (beneficial or not) for as long as the industry has existed. Managing the politics and the expectations of non-technical management is part of career development.
tokioyoyo
Yeah, I totally agree, and you're 100% right. But the amount of integrations I've personally done and have instructed my team to do implies this one will be around for a while. At some point spending too much time on code that could be easily generated will be a negative point on your performance.
I've heard exactly the same stories from my friends in larger tech companies as well. Every all hands there's a push for more AI integration, getting staff to use AI tools and etc., with the big expectation that development will get faster.
hansvm
"Yes, of course, I'm using AI at every single opportunity where I think it'll improve my output"
<<never uses AI>>
giobox
This simply doesn't work much of the time as an excuse - virtually all the AI tool subscriptions for corporations provide per user stats on how much each staff member is using the AI assist. This shouldn't come as a surprise - software tool purveyors need to demonstrate ROI to their customer's management teams and as always this is in reporting tools.
I've already seen several rounds of slacks: "why aren't you using <insert LLM coding assistant name>?" off the back of this reporting.
These assistants essentially spy on you working in many cases, if the subscription is coming from your employer and is not a personal account. For one service, I was able to see full logging of all the chats every employee ever had.
v3xro
As a former "staff engineer" these executives can go and have their careers and leave people who want to have code they can understand, reason about and focus on quality software well alone.
hinkley
When IntelliJ was young the autocomplete and automated refactoring were massive game changers. It felt like a dawn of a new age. But then release after release no new refactorings materialized. I don't know if they hit the Pareto limit or the people responsible moved on to new projects.
I think that's the sort of spot where better tools might be appropriate. I know what I want to do, but it's a mess to do it. I suspect that will be better at facilitating growth instead of stunting it.
jiggawatts
Hmm… I wonder if there will be a category of LLM-assisted refactoring tools that combine mechanistic transformations with the more flexible capabilities of generative AI. E.g.: update the English text in comments automatically to reflect code structure changes.
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aprilthird2021
> executives, directors and managerial staff think the opposite
The entire reason they hire us is to let them know if what they think makes sense. No one is ideologically opposed to AI generated code. It comes with lots of negatives and caveats that make relying on it costly in ways we can easily show to any executives, directors, etc. who care about the technical feasibility of their feelings.
the_mitsuhiko
> but using code generated from an LLM is pure madness unless what you are building is truly going to be thrown away and rewritten from scratch, as is relying on it as a linting, debugging, or source of truth tool.
That does not match my experience at all. You obviously have to use your brain to review it, but for a lot of problems LLMs produce close to perfect code in record time. It depends a lot on your prompting skills though.
xmprt
Perhaps I suck at prompting but what I've noticed is that if an LLM has hallucinated something or learned a fake fact, it will use that fact no matter what you say to try to steer it away. The only way to get out of the loop is to know the answer yourself but in that case you wouldn't need an LLM.
liamwire
I’ve found a good way to get unstuck here is to use another model, either or comparable or superior quality, or interestingly sometimes even a weaker version of the same product (e.g. Claude Haiku, vs. Sonnet*). My mental model here is similar to pair programming or simply bringing in a colleague when you’re stuck.
*I don’t know to what extent it’s worthwhile discussing whether you could call these the same model vs. entirely different, for any two products in the same family. Outside of simply quantising the same model and nothing else. Maybe you could include distillations of a base model too?
codr7
I would say prompting skills relative coding skills; and the more you rely on them, the less you learn.
the_mitsuhiko
That is not my experience. I wrote recently [1] about how I use it and it’s more like an intern, pair programmer or rubber duck. None of which make you worse.
axlee
What's your stack ? I have the complete opposite experience. LLMs are amazing at writing idiomatic code, less so at dealing with esoteric use cases.
And very often, if the LLM produces a poopoo, asking it to fix it again works just well enough.
Bjartr
> asking it to fix it again works just well enough.
I've yet to encounter any LLM from chatGPT to cursor, that doesn't choke and start to repeat itself and say it changed code when it didn't, or get stuck changing something back and forth repeatedly inside of 10-20 minutes. Like just a handful of exchanges and it's worthless. Are people who make this workflow effective summarizing and creating a fresh prompt every 5 minutes or something?
simonw
One of the most important skills to develop when using LLMs is learning how to manage your context. If an LLM starts misbehaving or making repeated mistakes, start a fresh conversation and paste in just the working pieces that are needed to continue.
I estimate a sizable portion of my successful LLM coding sessions included at least a few resets of this nature.
NicuCalcea
Hm, I use LLMs almost daily, and I've never had it say it changed code and not do it. If anything, they will sometimes try to "improve" parts of the code I didn't ask them to modify. Most times I don't mind, and if I do, it's usually a quick edit to say "leave that bit alone" and resubmit.
> Are people who make this workflow effective summarizing and creating a fresh prompt every 5 minutes or something?
I work on one small problem at a time, only following up if I need an update or change on the same block of code (or something very relevant). Most conversations are fewer than five prompt/response pairs, usually one-three. If the LLM gets something wrong, I edit my prompt to explain what I want better, or to tell it not to take a specific approach, rather than correcting it in a reply. It gets a little messy otherwise, and the AI starts to trip up on its own past mistakes.
If I move on to a different (sub)task, I start a new conversation. I have a brief overview of my project in the README or some other file and include that in the prompt for more context, along with a tree view of the repository and the file I want edited.
I am not a software engineer and I often need things explained, which I tell the LLM in a custom system prompt. I also include a few additional instructions that suit my workflow, like asking it to tell me if it needs another file or documentation, if it doesn't know something, etc.
Aeolun
Creating a new prompt. Sometimes it can go for a while without, but the first response (with crafted context) is generally the best. Having context from the earlier conversation has its uses though.
knicholes
The LLM you choose to work with in Cursor makes a big difference, too. I'm a fan of Claude 3.5 Sonnet.
brandall10
It's helpful to view working solutions and quality code as separate things to the LLM.
* If you ask it to solve a problem and nothing more, chances are the code isn't the best as it will default to the most common solutions in the training data.
* If you ask it to refactor some code idiomatically, it will apply most common idiomatic concepts found in the training data.
* If you ask it to do both at the same time you're more likely to get higher quality but incorrect code.
It's better to get a working solution first, then ask it to improve that solution, rinse/repeat in smallish chunks of 50-100 loc at a time. This is kinda why reasoning models are of some benefit, as they allow a certain amount of reflection to tie together disparate portions of the training data into more cohesive, higher quality responses.
doug_durham
I've had exactly the opposite experience with generating idiomatic code. I find that the models have a lot of information on the standard idioms of a particular language. If I'm having to write in a language I'm new in, I find it very useful to have the LLM do an idiomatic rewrite. I learn a lot and it helps me to get up to speed more quickly.
null
qqtt
I wonder if there is a big disconnect partially due to the fact that people are talking about different models. The top tier coding models (sonnet, o1, deepseek) are all pretty good, but it requires paid subscriptions to make use of them or 400GB of local memory to run deepseek.
All the other distilled models and qwen coder and similar are a large step below the above models in terms of most benchmarks. If someone is running a small 20GB model locally, they will not have the same experience as those who run the top of the line models.
Aeolun
The top of the line models are really cheap though. Getting an anthropic key and $5 of credit costs you exactly that, and gives you hundreds of prompts.
jondwillis
It isn't like you can't write tests or reason about the code, iterate on it manually, just because it is generated. You can also give examples of idioms or patterns you would like to follow. It isn't perfect, and I agree that writing code is the best way to build a mental model, but writing code doesn't guarantee intuition either. I have written spaghetti that I could not hope to explain many times, especially when exploring or working in a domain that I am unfamiliar with.
ajmurmann
I described how I liked doing ping-pong pairing TDD with Cursor elsewhere. One of the benefits of that approach is that I write at least half the implementation and tests and review every single line. That means that there is always code that follows the patterns I want and it's right there for the LLM to see and base its work on.
Edit: fix typo in last sentence
scudsworth
i love when the llm can be its work of
arijo
LLMs can work if you program above the code.
You still need to state your assertions with precision and keep a model of the code in your head.
Its possible to be be precise at an higher level of abstraction as long as your prompts are consistent with a coherent model of the code.
elliotto
> Its possible to be be precise at an higher level of abstraction as long as your prompts are consistent with a coherent model of the code.
This is a fantastic quote and I will use this. I describe the future of coding as natural language coding (or maybe syntax agnostic coding). This does not mean that the llm is a magic machine that understands all my business logic. It means what you've described - I can describe my function flow in abstracted english rather than requiring adherence to a syntax
simonw
On last resort bug fixes:
> I don’t do this a lot, but sometimes when I’m really stuck on a bug, I’ll attach the entire file or files to Copilot chat, paste the error message, and just ask “can you help?”
The "reasoning" models are MUCH better than this. I've had genuinely fantastic results with this kind of thing against o1 and Gemini Thinking and the new o3-mini - I paste in the whole codebase (usually via my https://github.com/simonw/files-to-prompt tool) and describe the bug or just paste in the error message and the model frequently finds the source, sometimes following the path through several modules to get there.
Here's a slightly order example: https://gist.github.com/simonw/03776d9f80534aa8e5348580dc6a8... - finding a bug in some Django middleware
elliotto
I have picked up the cursor tool which allows me to throw in relevant files with a drop down menu. Previously I was copy pasting files into the chatgpt browser page, but now I point cursor to o1 and do it within the ide.
One of my favourite things is to ask it if it thinks there are any bugs - this helps a lot with validating any logic that I might be exploring. I recently ported some code to a different environment with slightly different interfaces and it wasn't working - I asked o1 to carefully go over each implementation in detail why it might be producing a different output. It thought for 2 whole minutes and gave me a report of possible causes - the third of which was entirely correct and had to do with how my environment was coercing pandas data types.
There have been 10 or so wow moments over the past few years where I've been shocked by the capabilities of genai and that one made the list.
powersnail
The "attach the entire file" part is very critical.
I've had the experience of seeing some junior dev posting error messages into ChatGPT, applying the suggestions of ChatGPT, and posting the next error message into ChatGPT again. They ended up applying fixes for 3 different kinds of bugs that didn't exist in the code base.
---
Another cause, I think, is that they didn't try to understand any of those (not the solutions, and not the problems that those solutions are supposed to fix). If they did, they would have figured out that the solutions were mismatches to what they were witnessing.
There's a big difference between using LLM as a tool, and treating it like an oracle.
theshrike79
This is why in-IDE LLMs like Copilot are really good.
I just had a case where I was adding stuff to two projects, both open at the same time.
I added new fields to the backend project, then I swapped to the front-end side and the LLM autocomplete gave me 100% exactly what I wanted to add there.
And similar super-accurate autocompletes happen every day for me.
I really don't understand people who complain about "AI slop", what kind of projects are they writing?
jppope
My experience is similar: great for boilerplate, great for autocomplete, starts to fall apart on complex tasks, doesn't do much as far as business logic (how would it know?)- All in all very useful, but not replacing a decent practitioner any time soon.
LLMs can absolutely bust out some corporate docs super crazy fast too... probably a reasonable thing to re-evaluate the value though
nicksergeant
I've had kind of great experiences even doing complex tasks with lots of steps, as long as I tell it to take things slowly and verify each step.
I had a working and complete version of Apple MapKit JS rendering a map for an address (along with the server side token generation), and last night I told it I wanted to switch to Google Maps for "reasons".
It nailed it on the first try, and even gave me quick steps for creating the API keys in Google Dev Console (which is always _super_ fun to navigate).
As Simon has said elsewhere in these comments, it's all about the context you give it (a working example in a slightly different paradigm really couldn't be any better).
theshrike79
Exactly. For unit/integration tests I've found it to be a pretty good assistant.
I have a project with a bunch of tests already, then I pick a test file and write `public Task Test` and wait a few seconds, in most cases it writes down a pretty sane basis for a test - and in a few cases it figured out an edge case I missed.
delduca
> Disclaimer: I work for GitHub, and for a year I worked directly on Copilot.
Ah, now it makes sense.
brianstrimp
Yeah, the submission heading should indicate that there is a high risk for a sales pitch in there.
foobazgt
I wonder if the first bullet point, "smart auto complete", is much less beneficial if you're already using a statically typed language with a good IDE. I already feel like Intellij's auto complete reads my mind most of the time.
Klathmon
LLM autocomplete is an entirely different beast.
Traditional auto complete can finish the statement you started typing, LLMs often suggest whole lines before I even type anything, and even sometimes whole functions.
And static types can assist the LLM too. It's not like it's an either or choice
foobazgt
The author says they do the literal opposite:
"Almost all the completions I accept are complete boilerplate (filling out function arguments or types, for instance). It’s rare that I let Copilot produce business logic for me"
My experience is similar, except I get my IDE to complete these for me instead of an LLM.
baq
> Copilot
…needn’t say more.
Copilot was utter garbage when I switched to cursor+claude, it was like some alien tech upgrade at first.
neeleshs
I use LLM to generate complete solutions to small technical problems. "Write an input stream implementation that skips lines based on a regex".
Hard for an IDE auto complete to do this.
hansvm
It's completely different. If I start writing an itertools library with comptime inlining and my favorite selection of other features, completing map/reduce/take/skip/... exactly how I want them to look, LLM autocomplete can finish the rest of the library exactly as would have written it even for languages it doesn't otherwise know well, outside of the interesting bits (in the context of itertools, that'd be utilities with memory tradeoffs, like tee and groupby).
unregistereddev
Does IntelliJ try to complete the word you are typing, or does it suggest an entire line of code? Because newer versions of IntelliJ incorporate LLM's to beef up autocomplete. You may already be using it.
foobazgt
The new LLM-based completion in Intellij is not useful. :(
mrguyorama
I know I'm not using it because Intellij is constantly complaining that my version does not support the AI plugins.
The "dumb" autogenerated stuff is incredible. It's like going from bad autocomplete to Intellisense all over again.
The world of python tooling (at least as used by my former coworkers) put my expectations in the toilet.
n144q
Agree with many of the points here, especially the part with one-off, non-production code. I had great experience letting ChatGPT writing utility code. Once it provided Go code for an ad-hoc task which runs exactly as expected on first try, when it could cost me at least 30 minutes that's mostly spent on looking up APIs that I am not familiar with. Another time it created an HTTP server that worked with only minor tweaks. I don't want to think about life before LLMs existed.
One thing that is not mentioned -- code review. It is not great at it, often pointing out trivial or non issues. But if it finds 1 area for improvement out of 10 bullet points, that's still worth it -- most human code reviewers don't notice all the issues in the code anyway.
pgm8705
I used to feel they just served as a great auto complete or stack overflow replacement until I switched from VSCode to Cursor. Cursor's agent mode with Sonnet is pretty remarkable in what it can generate just from prompts. It is such a better experience than any of the AI tools VSCode provides, imo. I think tools like this when paired with an experienced developer to guide it and oversee the output can result in major productivity boosts. I agree with the sentiment that it falls apart with complex tasks or understanding unique business logic, but do think it can take you far beyond boilerplate.
Prickle
The main issue I am having here, is that I can see a measurable drop in my ability to write code because of LLM usage.
I need to avoid LLM use to ensure my coding ability stays up to par.
Aeolun
There’s no measurable drop in my ability to write code, but there’s a very significant one in my desire to.
stuartd
> is this idiomatic C?
This is how I use AI at work for maintaining Python projects, a language in which I am not at all really versed. Sometimes I might add “this is how I would do it in …, how would I do this in Python?”
I find this extremely helpful and productive, especially as I have to pull the code onto a server to test it.
synthc
This year I switched to a new job, using programming languages that I was less familiar with.
Asking a LLM to translate between languages works really well most of the time. It's also a great way to learn which libraries are the standard solution for a language. It really accelerated my learning process.
Sure, there is the occasional too literal translation or hallucination, but I found this useful enough.
brianstrimp
Have you noticed any difference in picking up the language(s) yourself? As in, do you think you'd be more fluent in it by now without all the help? Or perhaps less? Genuine question.
mewpmewp2
I do tons of TypeScript in my side projects and in real life, and I usually feel heavy frustrations when I stray away.
When I stray out of this (e.g. I started doing a lot of IoT, ML and Robotics projects, where I can't always use TypeScript). I think one key thing that LLMs have helped me is that I can ask why something is X without having to worry about sounding stupid or annoying.
So I think it has enabled me at least a way to get out of the TypeScript zone more worry free without losing productivity. And I do think I learn a lot, although I'm relating a lot of it on my JS/TS heavy experience.
To me the ability to ask stupid questions without fear of judgment or accidentally offending someone - it's just amazing.
I used to overthink a lot before LLMs, but they have helped me with that aspect, I think a lot.
I sometimes think that no one except LLMs would have the patience for me if I didn't filter my thoughts always.
hinkley
Some of the stuff in these explanations sounds like missing tools. There's a similar thread for a different article going around today, and I kept thinking at various points, "Maybe instead of having an LLM write you unit tests, you should check out Property Based Testing?"
The question I keep asking myself is, "Should we be making tools that auto-write code for us, or should we be using this training data to suss out the missing tools we have where everyone writes the same code 10 times in their careers?"
fosterfriends
"Proofreading for typos and logic mistakes: I write a fair amount of English documents: ADRs, technical summaries, internal posts, and so on. I never allow the LLM to write these for me. Part of that is that I think I can write more clearly than current LLMs. Part of it is my general distaste for the ChatGPT house style. What I do occasionally do is feed a draft into the LLM and ask for feedback. LLMs are great at catching typos, and will sometimes raise an interesting point that becomes an edit to my draft."
--
I work on Graphite Reviewer (https://graphite.dev/features/reviewer). I'm also partly dyslexic. I lean massively on Grammarly (using it to write this comment) and type-safe compiled languages. When I engineered at Airbnb, I caused multiple site outages due to typos in my ruby code that I didn't see and wasn't able to execute before prod.
The ability for LLMs to proofread code is a godsend. We've tuned Graphite Reviewer to shut up about subjective stylistic comments and focus on real bugs, mistakes, and typos. Fascinatingly, it catches a minor mistake in ~1/5 PRs in prod at real companies (we've run it on a few million PRs now). Those issues it catches result in a pre-merge code change 75% of the time, about equal to what a human comment does.
AIs aren't perfect, but Im thrilled that they work as fancy code spell-checkers :)
elwillbo
I'm in your boat with having to write a significant amount of English documents. I always write them myself, and have ChatGPT analyze them as well. I just had a thought - I wonder if I could paste in technical documentation, and code, to validate my documentation? Will have to try that later.
CoPilot is used for simple boilerplate code, and also for the autocomplete. It's often a starting point for unit tests (but a thorough review is needed - you can't just accept it, I've seen it misinterpret code). I started experimenting with RA.Aid (https://github.com/ai-christianson/RA.Aid) after seeing a post on it here today. The multi-step actions are very promising. I'm about to try files-to-prompt (https://github.com/simonw/files-to-prompt) mentioned elsewhere in the thread.
For now, LLMs are a level-up in tooling but not a replacement for developers (at least yet)
iamwil
I'd been using 4o and 3o to read research papers and ask about topics that are a little bit out of my depth for a while now. I get massive amount of value out of that. What used to take me a week of googling and squinting at wikipedia or digging for slightly less theoretical blog posts, I get to just upload a paper or transcript of a talk and just keep asking it questions until I feel like I got all the insights and ah-ha moments.
At the end, I ask it to give me a quiz on everything we talked about and any other insights I might have missed. Instead of typing out the answers, I just use Apple Dictation to transcribe my answers directly.
It's only recently that I thought to take the conversation I just had, and have it write a blog post of the insights and ah-ha moments I had, and have it write a blog post. It takes a fair bit of curation to get it to do that, however. I can't just say, "write me a blog post on all we talked about". I have to first get it to write an outline with the key insights. And then based on the outline, write each section. And then I'll use chatgpt's canvas to guide and fine-tune each section.
However, at no point do I have to specifically write the actual text. I mostly do curation.
I feel ok about doing this, and don't consider it AI slop, because I clearly mark at the top that I didn't write a word of it, and it's the result of a curated conversation with 4o. In addition, I think if most people do this as a result of their own Socratic methods with an AI, it'd build up enough training data for next generation of AI to do a better job of writing pedagogical explanations, posts, and quizzes to get people learning topics that are just out of reach, but there hadn't been too many people able to bridge the gap.
The two I had it write are: Effects as Protocols and Contexts as Agents: https://interjectedfuture.com/effects-as-protocols-and-conte...
How free monads and functors represent syntax for algebraic effects: https://interjectedfuture.com/how-the-free-monad-and-functor...
lantry
> I feel ok about doing this, and don't consider it AI slop, because I clearly mark at the top that I didn't write a word of it
This is key - if it's marked clearly as AI-generated or assisted, it's not slop. I think this is an important part of AI ethics that most people can agree with.
As a fellow "staff engineer" LLMs are terrible at writing or teaching how to write idiomatic code, and they are actually causing me to spend more time reviewing than I was previously due to the influx of junior to senior engineers trying to sneak in LLM garbage.
In my opinion, using LLMs to write code comes as a faustian deal where you learn terrible practices and rely on code quantity, boilerplate, and indeterministic outputs - all hallmarks of poor software craftsmanship. Until ML can actually go end to end on requirements to product and they fire all of us, you can't cut corners on building intuition as a human by forgoing reading and writing code yourself.
I do think that there is a place for LLMs in generating ideas or exploring an untrusted knowledge base of information, but using code generated from an LLM is pure madness unless what you are building is truly going to be thrown away and rewritten from scratch, as is relying on it as a linting, debugging, or source of truth tool.