How I use LLMs as a staff engineer

https://www.seangoedecke.com/how-i-use-llms/

by gfysfm • 1 year ago

248 195 1 year ago

stuartd• 1 year ago

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.

synthc3• 1 year ago

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.

brianstrimp5• 1 year ago

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.

foobazgt3• 1 year ago

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• 1 year ago

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

foobazgt3• 1 year ago

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.

unregistereddev• 1 year ago

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.

foobazgt3• 1 year ago

The new LLM-based completion in Intellij is not useful. :(

mrguyorama2• 1 year ago

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.

hansvm2• 1 year ago

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).

delduca• 1 year ago

Disclaimer: I work for GitHub, and for a year I worked directly on Copilot.

Ah, now it makes sense.

brianstrimp5• 1 year ago

Yeah, the submission heading should indicate that there is a high risk for a sales pitch in there.

arcticfox• 1 year ago

How I use LLMs as a staff engineer

With all the talk of o1-pro as a superb staff engineer-level architect, it took me awhile to re-parse this headline to understand what the author, apparently a staff engineer, meant

iamwil2• 1 year ago

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• 1 year ago

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.

iamwil2• 1 year ago

Not just that. I also spent quite a bit of time curating what it focused on. The insights and ah-ha moments.

brianstrimp5• 1 year ago

"as a staff engineer"

Such an unnecessary flex.

simonw11• 1 year ago

It's entirely relevant here. The opinions of a staff engineer on this stuff should be interpreted very differently from the opinions of a developer with much less experience.

phist_mcgee• 1 year ago

Reminds me of TechLead

https://www.youtube.com/watch?v=AbUU-D2Hil0

devmor2• 1 year ago

It's not relevant here, because this is a post from someone who worked on copilot. It's a shady sales pitch, disguised as an engineer's honest opinion.

theoryofx• 1 year ago

Not really because "Staff software engineer" has become the new "Senior Software Engineer" due to title inflation. It's become an essentially meaningless distinction at many companies.

Case in point, this person has around around 7 years of professional experience at just two companies, Zendesk and GitHub. I don't mean this as a personal dig in any way (truly) but this simply isn't what we used to mean by a "Staff" level software engineer.

This person is early-mid career, which we used to just call "Software engineer" then "Senior Software Engineer" and now (often enough) "Staff Software Engineer"

whoknowsidont• 1 year ago

Uhhh, I hate to break it to you but titles mean absolutely nothing in this industry.

twalla• 1 year ago

Not really, once you get past senior the “shape” of staff+ engineers varies greatly. At that level the scope is typically larger which can limit the usefulness of LLMs - I’d agree that the greatest value I’ve gotten is from being able to quickly get up to speed on something I’ve been asked to have an opinion on and sanity checking my work if I’m using an unfamiliar language or framework.

It also helps if you realize staff+ is just a way to financially reward people who don’t want to be managers so you end up with these unholy engineer/architect/project manager/product manager hybrids that have to influence without authority.

nmat• 1 year ago

To be honest I was expecting the article to focus primarily on internal docs, meetings, long Slack posts, etc. Staff engineers spend a relatively small percentage of their time writing code. A lot of what it takes to be successful is knowing how to communicate with different audiences which AI should be really useful for.

mns• 1 year ago

What is a Staff engineer anyhow? Sometimes I fell like all these titles and roles pop up all of the sudden to replace already existing ones that were already too boring.

simonw11• 1 year ago

A lot of companies use it for their IC track (Individual Contributir) track, to solve the problem of engineers being forced to move into management because otherwise their career progression stops.

I like this definition (which comes with a whole book): https://staffeng.com/

jppope2• 1 year ago

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• 1 year ago

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).

jppope2• 1 year ago

totally agree, what you are saying is aligned. The LLM needs you in the drivers seat, it can't do it with out you

theshrike794• 1 year ago

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.

callamdelaney• 1 year ago

My experience of copilot is that it’s completely useless and almost completely incapable of anything. 4o is reasonable though.

ddgflorida• 1 year ago

You summed it up well and your experience matches mine.

nvarsj2• 1 year ago

I'm trying to understand the point of the affix "as a staff engineer", but I cannot.

piuantiderp• 1 year ago

Anytime you read "as an X", spidey senses should tingle and be careful. Caveat lector

sangnoir• 1 year ago

Boosting their personal brand, perhaps?

nvarsj2• 1 year ago

Yes perhaps. I guess we all need to hustle for that F U money :).

pgm8705• 1 year ago

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• 1 year ago

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.

Aeolun6• 1 year ago

There’s no measurable drop in my ability to write code, but there’s a very significant one in my desire to.

t8sr• 1 year ago

I guess I'm officially listed as a "staff engineer". I have been at this for 20 years, and I work with multiple teams in pretty different areas, like the kernel, some media/audio logic, security, database stuff... I end up alternating a lot between using Rust, Java, C++, C, Python and Go.

Coding assistant LLMs have changed how I work in a couple of ways:

1) They make it a lot easier to context switch between e.g. writing kernel code one day and a Pandas notebook the next, because you're no longer handicapped by slightly forgetting the idiosyncrasies of every single language. It's like having smart code search and documentation search built into the autocomplete.

2) They can do simple transformations of existing code really well, like generating a match expression from an enum. They can extrapolate the rest from 2-3 examples of something repetitive, like converting from Rust types into corresponding Arrow types.

I don't find the other use cases the author brings up realistic. The AI is terrible at code review and I have never seen it spot a logic error I missed. Asking the AI to explain how e.g. Unity works might feel nice, but the answers are at least 40% total bullshit and I think it's easier to just read the documentation.

I still get a lot of use out of Copilot. The speed boost and removal of friction lets me work on more stacks and, consequently, lead a much bigger span of related projects. Instead of explaining how to do something to a junior engineer, I can often just do it myself.

I don't understand how fresh grads can get use out of these things, though. Tools like Copilot need a lot of hand-holding. You can get them to follow simple instructions over a moderate amount of existing code, which works most of the time, or ask them to do something you don't exactly know how to do without looking it up, and then it's a crapshoot.

The main reason I get a lot of mileage out of Copilot is exactly because I have been doing this job for two decades and understand what's happening. People who are starting in the industry today, IMO, should be very judicious with how they use these tools, lest they end up with only a superficial knowledge of computing. Every project is a chance to learn, and by going all trial-and-error with a chatbot you're robbing yourself of that. (Not to mention the resulting code is almost certainly half-broken.)

chasd00• 1 year ago

This is pretty much how i use llm for coding. I already know what i want i just don't want to type it out. I ask the llm to do the typing for me and then i check it over, copy/paste it in, and make any adjustments or extensions.

theshrike794• 1 year ago

This is the way.

Just last night I did a quick test on Cursor (first time trying it). Opened up my IRC bot project and asked it to "add relevant handlers for IRC messages".

It immediately recognised the pattern I had used before and added CTCP VERSION, KICK, INVITE and 433 (nickname already in use). It didn't try to add everything under the sun and just added those. Took me 20 seconds.

toprerules• 1 year ago

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.

doug_durham• 1 year ago

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.

qqtt• 1 year ago

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.

baq3• 1 year ago

I’ve iterated on 1k lines of react slop in 4h the other day, changed table components twice, handled errors, loading widgets, modals, you name it. It’d take me a couple days easily to get maybe 80% of that done.

The result works ok, nobody cares if the code is good or bad. If it’s bad and there are bugs, doesn’t matter, no humans will look at it anymore - Claude will remix the slop until it works or a new model will rewrite the whole thing from scratch.

Realized during writing this that I should’ve added the extract of requirements in the comment of the index.ts of the package, or maybe a README.CURSOR.md.

hollowturtle• 1 year ago

I'd pay to review one of your PRs. Maybe a consistent one with ai usage proof.

mrtesthah2• 1 year ago

My experience having Claude 3.5 Sonnet or Google Gemini 2.0 Exp-12-06 rewrite a complex function is that it slowly introduces slippage of the original intention behind the code, and the more rewrites or refactoring, the more likely it is to do something other than what was originally intended.

At the absolute minimum this should require including a highly detailed function specification in the prompt context and sending the output to a full unit test suite.

tokioyoyo9• 1 year ago

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.

rectang3• 1 year ago

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.

hansvm2• 1 year ago

"Yes, of course, I'm using AI at every single opportunity where I think it'll improve my output"

<<never uses AI>>

feoren• 1 year ago

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.

subw00f2• 1 year ago

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.

hinkley3• 1 year ago

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.

v3xro• 1 year ago

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.

aprilthird2021• 1 year ago

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.

billy99k• 1 year ago

I've seen the exact opposite. Management at my company has been trying to shove AI into everything. They even said that this year we would be dropping all vendors that didn't have some for of AI in their workflow.

jondwillis• 1 year ago

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.

ajmurmann2• 1 year ago

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

axlee• 1 year ago

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• 1 year ago

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?

brandall10• 1 year ago

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.

the_mitsuhiko5• 1 year ago

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.

codr79• 1 year ago

I would say prompting skills relative coding skills; and the more you rely on them, the less you learn.

xmprt• 1 year ago

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.

icnexbe7• 1 year ago

i’ve had some luck with asking conceptual questions about how something works if i am using library X with protocol Y. i usually get an answer that is either actually useful or at least gets me on the right path of what the answer should be. for code though, it will tell me to use non existent apis from that library to implement things

arijo• 1 year ago

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.

elliotto2• 1 year ago

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

beepbooptheory• 1 year ago

Simply, it made my last job so nightmarish that for the first time in this career I absolutely dreaded even thinking about the codebase or having to work the next day. We can argue about the principle of it all day, or you can say things like "you are just doing it wrong," but ultimately there is just the boots-on-the-ground experience of it that is going to leave the biggest impression on me, at least. Like it's just so bad to have to work alongside, either the model itself or your coworker with the best of intentions but no domain knowledge.

Its like having to forever be the most miserable detective in the world; no mystery, only clues. A method that never existed, three different types that express the same thing, the cheeky smile of your coworker who says he can turn the whole backend into using an ORM in a day because he has Cursor, the manager who signs off on this, the deranged PR the next day. This continual sense that less and less people even know whats going on anymore...

"Can you make sure we support both Mongo and postgres?"

"Can you put this React component inside this Angular app?"

"Can you setup the kubernetes with docker compose?"

esafak2• 1 year ago

Hiring standards are important, as are managers who get it. Your organization seems to be lacking in both.

the_real_cher• 1 year ago

Code written from an LLM is really really good if done right, i.e. reviewing every line of code as it comes out and prompt guiding it in the right direction.

If youre getting junior devs just pooping out code and sending to review thats really bad and should be a pip-able offense in my opinion.

satellite2• 1 year ago

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.

purerandomness• 1 year ago

If the next 5 lines of code are so predictable, do they really need to be written down?

If you're truly saving time by having an LLM write boiler plate code, is there maybe an opportunity to abstract things away so that higher-level concepts, or more expressive code could be used instead?

whatever1• 1 year ago

The counterargument that I hear is that since writing code is now so easy and cheap, there is no need to write pretty code that generalizes well. Just have the llm write a crappy version and the necessary tests, and once your requirements change you just toss everything and start fresh.

why-el• 1 year ago

I was hoping the LLM is the staff engineer? can read both ways.

mvdtnz2• 1 year ago

What about hallucinations? Honestly, since GPT-3.5, I haven’t noticed ChatGPT or Claude doing a lot of hallucinating.

See this is what I don't get about the AI Evangelists. Every time I use the technology I am astounded at the amount of incorrect information and straight up fantasy it invents. When someone tells me that they just don't see it, I have to wonder what is motivating them to lie. There is simply no way you're using the same technology as me with such wildly different results.

mrguyorama2• 1 year ago

I have to wonder what is motivating them to lie.

Most of these people who aren't salesmen aren't lying.

They just cannot tell when the LLM is making up code. Which is very very sad.

That or they could literally be replaced by a script that copy/pastes from stack-overflow. My friend did that a lot and it definitely helped features ship but doesn't make maintainable code.

simonw11• 1 year ago

There is simply no way you're using the same technology as me with such wildly different results.

Prompting styles are incredibly different between different people. It's very possible that they are using the same technology that you are with wildly different results.

I think learning to use LLMs to their maximum effectiveness takes months (maybe even years) of effort. How much time have you spent with them so far?

the_mitsuhiko5• 1 year ago

When someone tells me that they just don't see it, I have to wonder what is motivating them to lie. There is simply no way you're using the same technology as me with such wildly different results.

I don’t know what technology you are using but I know that I am getting very different results based on my own prompt qualities.

I also do not really consider hallucinations to be much of an issue for programming. It comes up so rarely and it’s caught by the type checker almost immediately. If there are hallucinations it’s often very minor things like imagining a flag that doesn’t exist.

mvdtnz2• 1 year ago

A lot of you guys, including the author, will respond with these "you're holding it wrong" comments. But you never give examples of actual prompts that are somehow significantly different to what I use. The author gives a very small handful of his example prompts and I don't see anything in them that's fundamentally different to what I've tried. If anything his prompts are especially lazy compared to what I use and what I have read as best practice among "prompt engineers":

"is this idiomatic C?"

"not just “how does X work”, but follow-up questions like “how does X relate to Y”. Even more usefully, you can ask “is this right” questions"

"I’ll attach the entire file or files to Copilot chat, paste the error message, and just ask “can you help?”"

dlvhdr• 1 year ago

Another article that doesn’t introduce anything new

softwaredoug• 1 year ago

I think people mistakenly use LLMs as research tools, thinking in terms of search, when they're better as collaborators / co-creators of scaffolding you know you need to edit.

ur-whale• 1 year ago

This article completely correlates with my so far very positive experience of using LLM's to assist me in writing code.

simonw11• 1 year ago

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

powersnail• 1 year ago

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.

theshrike794• 1 year ago

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?

elliotto2• 1 year ago

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.

n144q4• 1 year ago

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.

elwillbo• 1 year ago

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)

devmor2• 1 year ago

How I use LLMs as a senior engineer:

1. Try to write some code

2. Wonder why my IDE is providing irrelevant, confusing and obnoxious suggestions

3. Realize the AI completion plugin somehow turned itself back on

4. Turn it off

5. Do my job better than everyone that didn't do step 4

ggregoire• 1 year ago

Don't people actually enjoy writing code and solving problems on their own?

I would be so bored if my job consisted of writing prompts all day long.

simonw11• 1 year ago

I use LLMs to support my programming all day, and I'm not "writing prompts all day long". I'm working just like I used to, only faster.

LLMs make it quicker for me to:

- Decipher obscure error messages

- Knock out a quick exploratory prototype of a new idea, both backend and frontend code

- Write boiler plate code against commonly used libraries

- Debug things: feeding a gnarly bug plus my codebase into Gemini (for long context) or o3-mini can save me a TON of frustration

- Research potential options for libraries that might help with a problem

- Refactor - they're so good at refactoring now

- Write tests. Sometimes I'll have the LLM sketch out a bunch of tests that cover branches I may have not bothered to cover otherwise.

I enjoy working like this a whole lot more than I enjoyed working without them, and I enjoyed programming a lot prior to LLMs.

Aeolun6• 1 year ago

I enjoy building things. Writing code, well, I could take or leave that.

n144q4• 1 year ago

Using LLMs to write code for you is solving problems. The argument is almost like saying "using a third party library is not solving a problem on your own". If it gets the job done, it works.

I enjoy writing code, but I enjoy seeing getting a feature out even more. In fact, I don't quite enjoy the part of writing basic logic or tweaking CSS which an intern can easily do.

I don't think anybody is writing prompts all day long. If you don't actually know how to write code, maybe. But at this point, a professional software engineer still works with a code base with a hands-on approach most of the time, and even heavy LLM users still spend a lot of time hand writing code.

fosterfriends• 1 year ago

"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 :)

hinkley3• 1 year ago

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?"

prisenco• 1 year ago

I used Copilot for a while (since the beta whenever that was) but recently I stopped and I'm glad I did. I use Claude and DeepSeek for searching documentation and rubber ducking/pair programming style conversations when I'm stuck, but that's about it.

I stick to a "no copy & paste" rule and that includes autocomplete. Interactions are a conversation but I write all my code myself.

asdev• 1 year ago

if you write your code with good dependency injection/abstraction, you can one shot unit tests a lot of the time

bsder• 1 year ago

The big problem with LLMs as a "staff engineer" is that LLMs are precisely suited to the kind of tasks that I would normally assign to a junior engineer or cooperative engineering student.

That's bad because it makes "not training your juniors" the default path for senior people.

I can assign the task to one of my junior engineers and they will take several days of back and forth with me to work out the details--that's annoying but it's how you train the next generation.

Or I can ask the LLM and it will spit back something from its innards that got indexed from Github or StackOverflow. And for a "junior engineer" task it will probably be correct with the occasional hallucination--just like my junior engineers. And all I have to do for the LLM is click a couple of keys.

VenturingVole2• 1 year ago

My first thought upon reading this was the observation about the fact software engineers are deeply split: How can they be so negative? A mixture of emotions.

Then I reflected, how very true it was. In fact, as of writing this there are 138 comments and I started simply scrolling through what was shown to assess the negative/neutral/positive bias based upon a highly subjective personal assessment: 2/3 were negative and so I decided to stop.

As a profession, it seems many of us have become accustomed to dealing in absolutes when reality is subjective. Judging LLMs prematurely with a level of perfectionism not even cast upon fellow humans.. or at least, if cast upon humans I'd be glad not to be their colleagues.

Honestly right now - I would use this as a litmus test in hiring and the majority would fail based upon their closed-mindedness and ability to understand how to effectively utilise tools at their disposal. It won't exist as a signal for much longer, sadly!

notTooFarGone• 1 year ago

It boils down to responsibility.

We need to trust machines more than humans because machines can't get responsibility. That code that you pushed and broke prd - you can't point at the machine.

It is also predictability/growth in a sense. I can assess certain people and know what they will probably get wrong and develop the person and adjust it. If that person uses LLMs it disguises that exposure of skill and leads to a very hard signal to read as a senior dev, hampering their growth.

VenturingVole2• 1 year ago

I absolutely agree with your points - assuming that "machines" to mean the code as opposed to the LLMs: As a "Staff+" IC type and mentoring and training a couple of apprentice-level developers I've already asked on several occasions "why did you do this?" and had a response of "oh, that's what the AI did." I'm very patient, but have made clear that's something to never utter - at least not until one has the experience to deeply understand boundaries/constraints effectively.

I did see a paper recently on the impact of AI/LLMs and danger to critical thinking skills - it's a very real issue and I'm having to actively counter this seemingly natural tendency many have.

With respect to signals, mine was around the attitude in general. I'd much rather work with someone who goes "Yes, but.." than one who is outright dismissive.

Increasing awareness of the importance of context will be a topic for a long time to come!

SquibblesRedux• 1 year ago

I have found LLMs to be "good enough" for:

- imprecise semantic search

- simple auto-completion (1-5 tokens)

- copying patterns with substitutions

- inserting commonly-used templates

floppiplopp• 1 year ago

Yes, LLMs are great at generating corporate bullshit to appease the clueless middle management. I wouldn't trust its code generation for production systems though, but I can see how inexperienced devs might do just that.

nbaugh1• 1 year ago

One thing I don't see mentioned often but is definitely true for me - I use Google like 90% less frequently now. I spend zero time crawling through various blogs or stack overflow questions to do things like understand an error I haven't seen before. Google is basically now a means of directing me to official docsites if I don't already know the URL

How I use LLMs as a staff engineer

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