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by apsurd11 hours ago |
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upvoteby phpnode10 hours ago|
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it does take a little while to get good at this new skill, yes. Just like, say, learning a new programming language and the ecosystem around it takes some effort. After you get over the hump it's really very straightforward and mostly a matter of knowing the kinds of mistakes the LLM is likely to make ahead of time, and then kindly asking it to do something smarter. If you've successfully mentored junior engineers you already have this skill.
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upvoteby apsurd10 hours ago|
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that's well put. But i'd stress mentoring junior engineers is really a high effort, high leverage, high demand skill. A good teacher is gold. and not common.
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upvoteby xmprt10 hours ago|
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I'll go in the other direction and say that if you're spending a lot of your time learning to prompt better then you're wasting it because LLMs are only going to get better at understanding your intent regardless of "prompt engineering". The JSON API example to wire up a database can be one-shot pretty easily by the latest models without much context and without setting up any harness. The more time you spend perfecting your harness, the more time you would have wasted when the next model comes out to make it obsolete.
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upvoteby majormajor9 hours ago|
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The hardest thing about software engineering has always been that your intent often has to be decided on the fly once you get into complicated edge cases, weird-or-legacy-business requirements, or things that the spec literally has no answers for.

Letting the tool figure out your assumed intent on those things is a double-edged sword. Better than you never even thinking of them. But potentially either subtle broken contracts that test coverage missed (since nobody has full combinatoric coverage, or the patience to run it) or just further steps into a messy codebase that will cost ever-more tokens to change safely.

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upvoteby derangedHorse10 hours ago|
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I was thinking of this interpretation as I read that:

"I'll go in the other direction and say that if you're spending a lot of your time learning to [program] better then you're wasting it because [computer]s are only going to get better at [computing] regardless of "[software] engineering". The JSON API example to wire up a database can be [run] pretty easily by the latest [computer]s without much [design] and without setting up any [optimizations]. The more time you spend perfecting your [program], the more time you would have wasted when the next [computer] comes out to make it obsolete."

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upvoteby apsurd10 hours ago|
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but then how can the parent comment land? "you're just not prompting right"
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upvoteby xmprt9 hours ago|
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I don't think it does. If I had to guess, the top comment was using an older version of AI or a local model which wouldn't be able to solve the JSON API task. A lot of AI skepticism comes from people who used it once a while back and decided not to keep up with the latest developments. If I only had experience with gpt-3.5 then I'd also assume what the original commenter said.
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upvoteby majormajor9 hours ago|
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An experiment I'd love to do, but which isn't actually possible anymore, is run GPT 3.5 or the original 4 API release through a modern "agentic" harness for a task like this.

I think 3.5 would probably need more frequent intervention than a lot of harnesses give. But I bet 4 could do a simple JSON API one-shot with the right harness. Just back then I had to manually be the harness.

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upvoteby eddieroger10 hours ago|
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I disagree it's a cop-out, but I agree it's hard to get good at writing prompts and takes a lot of effort. But so is programming. We're trading one skill set for another and getting a bigger return on it.

I started as a skeptic and have similarly drank the kool-aid. The reality is AI can read code faster than I can, including following code paths. It can build and keep more context than I can, and do it faster as well. And it can write code faster than I can type. So the effort to learn how to tell it what to do is worthwhile.

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upvoteby apsurd10 hours ago|
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yep fully agree. i'm taking issue with the flippant "not prompting right" as if they're holding it upside down vs it's actually a meaningful skill to have to invest in so it's fully believable that someone trained in normal code gen is much more proficient up front.
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upvoteby yakbarber10 hours ago|
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this seems disingenuous. even if your premise is true (which i don't think it is), it only really holds for the first few endpoints. most systems have many, and the models are very good at copying established patterns to the point that you wouldn't normally have to re-explain every detail for every endpoint. so you might be right for the first (you're not), but you're definitely wrong for the next 50.
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upvoteby majormajor9 hours ago|
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To be fair, I don't know many humans who would write endpoints 2-50 from scratch either in that situation.

Time-wise, it's easy-mode vs easy-mode at that point.

The human is more likely to make copypasta errors, though!

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