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Comment by brokencode | Hacker News Reader

Out of curiosity, what do you work on where you don’t have to experiment with different solutions to see what works best?

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Usually when we’re doing it in practice there’s _somewhat_ more awareness of the mechanics than just throwing random obstructions in and hoping for the best.

LLMs are still very young. We'll get there in time. I don't see how it's any different than optimizing for new CPU/GPU architectures other than the fact that the latter is now a decades-old practice.

Not to pick on you, but this is exactly the objectionable handwaving. What makes you think we'll get there? The kinds of errors that these technologies make have not changed, and anything that anyone learns about how to make them better changes dramatically from moment to moment and no one can really control that. It is different because those other things were deterministic ...

In comp sci it’s been deterministic, but in other science disciplines (eg medicine) it’s not. Also in lots of science it looks non-deterministic until it’s not (eg medicine is theoretically deterministic, but you have to reason about it experimentally and with probabilities - doesn’t mean novel drugs aren’t technological advancements).

And while the kind of errors hasn’t changed, the quantity and severity of the errors has dropped dramatically in a relatively short span of time.

The problem has always been that every token is suspect.

It's the whole answer being correct that's the important thing, and if you compare GPT 3 vs where we are today only 5 years later the progress in accuracy, knowledge and intelligence is jaw dropping.

I have no idea what you're talking about because they still screw up in the exact same way as gpt3.

The hallucination quantity and severity is way less in new frontier models.

But not more predictable or regular.

> I don't see how it's any different than optimizing for new CPU/GPU architectures

I mean that seems wild to say to me. Those architectures have documentation and aren't magic black boxes that we chuck inputs at and hope for the best: we do pretty much that with LLMs.

If that's how you optimise, I'm genuinely shocked.

i bet if we talked to a real low level hardware systems/chip engineer they'd laugh and take another shot at how we put them on a pedestal

Not really, in my experience. There's still fundamental differences between designed systems and trained LLMs.

most people are building straightforward crud apps. no experimentation required.

[citation needed]

In my experience, even simple CRUD apps generally have some domain-specific intricacies or edge cases that take some amount of experimentation to get right.

Idk, it feels like this is what you’d expect versus the actual reality of building something.

From my experience, even building on popular platforms, there are many bugs or poorly documented behaviors in core controls or APIs.

And performance issues in particular can be difficult to fix without trial and error.

Not helpful when the llm knowledge cutoff is a year out of date and api and lib has been changed since

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