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1257 points adrianh | 2 comments | | HN request time: 0.417s | source
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kragen ◴[] No.44491713[source]
I've found this to be one of the most useful ways to use (at least) GPT-4 for programming. Instead of telling it how an API works, I make it guess, maybe starting with some example code to which a feature needs to be added. Sometimes it comes up with a better approach than I had thought of. Then I change the API so that its code works.

Conversely, I sometimes present it with some existing code and ask it what it does. If it gets it wrong, that's a good sign my API is confusing, and how.

These are ways to harness what neural networks are best at: not providing accurate information but making shit up that is highly plausible, "hallucination". Creativity, not logic.

(The best thing about this is that I don't have to spend my time carefully tracking down the bugs GPT-4 has cunningly concealed in its code, which often takes longer than just writing the code the usual way.)

There are multiple ways that an interface can be bad, and being unintuitive is the only one that this will fix. It could also be inherently inefficient or unreliable, for example, or lack composability. The AI won't help with those. But it can make sure your API is guessable and understandable, and that's very valuable.

Unfortunately, this only works with APIs that aren't already super popular.

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1. a_e_k ◴[] No.44494851[source]
I've played with a similar idea for writing technical papers. I'll give an LLM my draft and ask it to explain back to me what a section means, or otherwise quiz it about things in the draft.

I've found that LLMs can be kind of dumb about understanding things, and are particularly bad at reading between the lines for anything subtle. In this aspect, I find they make good proxies for inattentive anonymous reviewers, and so will try to revise my text until even the LLM can grasp the key points that I'm trying to make.

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2. kragen ◴[] No.44494877[source]
That's fantastic! I agree that it's very similar.

In both cases, you might get extra bonus usability if the reviewers or the API users actually give your output to the same LLM you used to improve the draft. Or maybe a more harshly quantized version of the same model, so it makes more mistakes.