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> Tutoring me on how to use poorly documented APIs

Good luck with that.

GPT-4 is _incredibly_ bad at Metal (most under-documented GPU api I've ever worked with), but more importantly it's _even worse_ at Vulkan, which is complete opposite - it's meticulously documented.

The stuff LLMs are good at is the stuff that a lot of people use and talk about all the time, in public.

There's just not a lot of public discussion (with examples) about e.g. how to properly use Vulkan in a production application, and the LLM itself can't "reason" within the framework of the detailed specification, so most things it outputs is wrong.

In the long run though, a very likely outcome is that tools that the LLMs have a poor "understanding" of just wither and die, or are relegated to being something only 3.5 people in the world use. I can see a future where literally all software is written in python just because that's the language that OpenAI's LLMs happens to know best.



It has an 8kb (or 32kb if you are lucky) context. Try giving it the relevant documentation examples before asking a question.


the point the grandparent was making was that GPT-4 can teach you how to use an under-documented API. that means the relevant docs are either sparse or non-existent, otherwise there wouldn't be a need to ask GPT-4 in the first place.


No, that wasn’t my point. You don’t need docs, just feed in the raw source code. GPT4 can make sense of code without documentation.


feed it raw source code of what exactly?




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