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An example of LLM prompting for programming

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Re: An example of LLM prompting for programming

#221

Post by nextworddev »

ZephyrBlu wrote: post #29
nextworddev wrote: post #18

Earlier quoted context omitted.

The opposite might be true, and here’s why - 1) by using English as spec, the barrier of entry has gone lower, 2) LLMs can also write prompts and self introspect to debug.

I think English as a spec actually makes the barrier of entry higher, not lower. Code itself is far easier to understand than an English description of the code. To understand an English description of code you already have to have a deeper understanding of what the code is doing. For code itself you can reference the syntax to understand what's going on. The prompt in this case is using very technical language that…

Yes but LLMs can also be used by laypeople to explain the issue in plain English too. That’s the problem. Not that LLMs would need human to guide the debugging process anyways (at least in a few years)

Re: An example of LLM prompting for programming

#222

Post by 62951413 »

wpietri wrote: post #152
themodelplumber wrote: post #8

If somebody thinks an LLM is coming for everybody's coding job, I'd say this article is a great counterpoint just for existing. You could tell someone from decades ago that we now use a very high level language for complex tasks in complex code ecosystems, never even mention AI, explain that the parser is really generalist-biased, and this article would make perfect sense as an example of exemplary code by a modern c…

Yeah, I think there's a "stone soup" effect going on with AI. It's the same sort of thing you see happening with the customers of psychics. People often have poor awareness of how much they're putting in to a conversation. Or it's a bit like the way Tom Sawyer tricks other kids into painting the fence for him. For me a lot of the magic here is in knowing what questions to ask and when the answers aren't right. If you…

I believe the Model Driven Architecture fad (https://en.wikipedia.org/wiki/Model-driven_architecture) is a better analogy than wizards. Back then the holy grail of complete round trip UML->code->UML didn't get practical enough to justify the effort.

Re: An example of LLM prompting for programming

#223

Post by ok_dad »

lcnPylGDnU4H9OF wrote: post #177
svachalek wrote: post #164

Earlier quoted context omitted.

You simply need the system to train itself on its own interactions, like how search engines improve results by counting clicks.

I'm not wondering about how the system will determine what's most helpful but instead determining what's even "correct". A model will learn what's "correct" from Stack Overflow by finding accepted or highly-voted answers but when it can't find such content anymore (in this case because Stack Overflow is hypothetically gone) then what would even exist to generate these discussions to be used as training data? Github,…

When Google search became important, people structured their information so that Google could best index it. When AIs become important in the same way, people will start to structure their information so that a particular class of AI can best index it. If that involves API documentation, perhaps there will be a standard format that AIs understand the best.

Re: An example of LLM prompting for programming

#224

Post by ZephyrBlu »

nextworddev wrote: post #221
ZephyrBlu wrote: post #29

Earlier quoted context omitted.

I think English as a spec actually makes the barrier of entry higher, not lower. Code itself is far easier to understand than an English description of the code. To understand an English description of code you already have to have a deeper understanding of what the code is doing. For code itself you can reference the syntax to understand what's going on. The prompt in this case is using very technical language that…

Yes but LLMs can also be used by laypeople to explain the issue in plain English too. That’s the problem. Not that LLMs would need human to guide the debugging process anyways (at least in a few years)

You still have the same problem... You cannot describe a technical field with plain English. If you did so the semantics would be incorrect. There is a reason jargon exists.

The first two paragraphs alone are absolutely chock with terms that would not be easily explained to a layperson:

"The current system is an online whiteboard system. Tech stack: typescript, react, redux, konvajs and react-konva. And vitest, react testing library for model, view model and related hooks, cypress component tests for view.

All codes should be written in the tech stack mentioned above. Requirements should be implemented as react components in the MVVM architecture pattern."

What is every library in that list? What is a model? What is a view model? What is a hook, component test, view, MVVM, etc?

If a layperson could understand explanations for all these things then they would not be a layperson.

Re: An example of LLM prompting for programming

#225

Post by wpietri »

baq wrote: post #179
wpietri wrote: post #152

Earlier quoted context omitted.

Yeah, I think there's a "stone soup" effect going on with AI. It's the same sort of thing you see happening with the customers of psychics. People often have poor awareness of how much they're putting in to a conversation. Or it's a bit like the way Tom Sawyer tricks other kids into painting the fence for him. For me a lot of the magic here is in knowing what questions to ask and when the answers aren't right. If you…

I mostly agree except one critical detail: LLMs are the low code/no code service. You literally tell them what you want and if they’re fine tuned on the problem domain, you’re all set. Microsoft demo’d the office 365 integration and if it works half as well in practice they’ll own the space as much as they have in 1997.

Maybe they will be, but that's not proven yet. We'll see! If anything, the article we're looking at suggests that the "tell them what you want" step is not obviously much less rigorous or effortful than coding. Tuning could make the difference, or it could be one of those things that produces better demos than results.

Re: An example of LLM prompting for programming

#226

Post by byt143 »

hn_throwaway_99 wrote: post #219
ryanjshaw wrote: post #199

Earlier quoted context omitted.

Your experience matches mine closely. I've had ChatGPT-4 do great and then it just gets confused after a while. I can literally tell it "task X is done" and it'll apologise and show me a list of tasks where X is still not done - this is clearly not just a context window issue, as I have repeated variations of my statement over and over in the same session and the issue persists. I have ended up using it the same way…

Hmm, I also use ChatGPT as an anti-procrastination tool and task manager, and it's never made a mistake with keeping track of my task list (except that when it sums the estimated times of subgroups of tasks, sometimes those sums are wrong). Note that it outputs my updated task list every time I add or remove a task (I only asked it to do that one time), so even if old messages go outside of the context window, it's n…

Interesting. What's your prompt?

Re: An example of LLM prompting for programming

#227

Post by abhibeckert »

majormajor wrote: post #195
tarruda wrote: post #190

Earlier quoted context omitted.

> The hype that LLMs are going to replace SWEs is clearly just that, hype LLMs cannot replace anyone, but it is clear that engineers which master LLMs usage might multiply their productivity by a lot. The question is: If one LLM assisted engineer can work 10x faster, will companies reduce their engineer staff by 90%?

I've worked at far more companies with miles of product idea backlog we never get to than ones with nothing for engineering to do. Now product will be able to use an LLM to come up with feature proposals and design docs even faster! :o So: are you working at a company where engineering is a cost center or a revenue center? The latter wants to get more done at the same cost much more than it wants to just cut spend.

I'm working at a company where we perpetually don't have enough engineers and we've learned (the hard way) that adding more tends to make the problem worse (too many cooks).

Copilot helps a lot with that. I can write `// check if em` and it will finish the comment (email is valid) and write the actual check.

It's a massive productivity boost. I'm spending less time looking stuff up (what was that function name again?) and less time fixing typos/basic syntax errors.

Copilot doesn't always know the function name, but it does more often than me. And it makes errors too, but again less often than me (also, because I'm reading the code, not typing it, I tend to see the error immediately without needing to test it).

Re: An example of LLM prompting for programming

#228

Post by riwsky »

tarruda wrote: post #190
rootusrootus wrote: post #155

Earlier quoted context omitted.

I agree it's a good idea to take a moderate approach. The hype that LLMs are going to replace SWEs is clearly just that, hype, if you've done any real work trying to get GPT4 to give you the code you want. But it's also clearly a very useful tool. I think it'll absolutely destroy Stack Overflow.

> The hype that LLMs are going to replace SWEs is clearly just that, hype LLMs cannot replace anyone, but it is clear that engineers which master LLMs usage might multiply their productivity by a lot. The question is: If one LLM assisted engineer can work 10x faster, will companies reduce their engineer staff by 90%?

> The question is: If one LLM assisted engineer can work 10x faster, will companies reduce their engineer staff by 90%?

What did you think “replacing SWEs” means?

Re: An example of LLM prompting for programming

#229

Post by all2 »

nice_byte wrote: post #165

do people enjoy working this way? wasting time verbalizing your thoughts, stating the obvious, wordsmithing to get the thing to "understand" what you _actually_ want?

Sometimes I don't know what I want, or I don't know if the way I want to do it is possible. For these paths, using ChatGPT is very useful. So far my use cases are 'taking care of boilerplate' and 'finding ways to do things'.

Re: An example of LLM prompting for programming

#230

Post by [deleted] »

MacsHeadroom wrote: post #208
nuancebydefault wrote: post #196

Earlier quoted context omitted.

To answer your question with a question if I may -- when did productivity increase in software ever result in headcount reduction? The competition also will have similar productivity gain.

>when did productivity increase in software ever result in headcount reduction? The competition also will have similar productivity gain. The average AI company has like 1 employee per $25M valuation. That's around 25x fewer employees than the typical tech company.

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