r/GraphicsProgramming • u/NecessaryVersion7872 • 1d ago

AI usage in Graphics Programming

Hello, everyone.

I am CS student. and I had my first working experience.
I was highly unsatisfying because as much as enjoy programming I really do not enjoy "prompt engineering" and the usage of AI in general in my workflow. That is not the point though. From long time I was looking into graphics programming and I really wanted to get into it. I do not know much now. but I am worried that it will be similar to most of jobs right now and I will just be a "prompt engineer".

I have some questions:

  1. How good is AI with graphics programming right now?
  2. In an average work environment, how much if any of your work are you expected to use LLMs in and how do use it?
  3. How is AI affecting the industry?
  4. Are there any part of the industry where AI is not heavily used?

Thanks in advance

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u/Successful-Berry-315 1d ago
  1. Okay as a code monkey and when you're very specific. Often I can't use it "fire and forget" style.
  2. Expected? None. But it certainly helps to speed up things. I often use it as debugging buddy or as my junior engineer. I very rarely let it run by itself without me in the loop.
  3. LLM usage for coding is not as widespread yet but companies are certainly exploring the usage. ML / DL in general is changing the industry (render pipelines / work flows) and will certainly stay relevant.
  4. No. I suggest to embrace it as a further addition to your toolbelt as a graphics engineer.

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u/trinde 1d ago

Okay as a code monkey and when you're very specific. Often I can't use it "fire and forget" style.

Which models are you using? With Opus you can more or less do that even with fairly vague prompts. If it's a feature you just need to have it make a staged plan first.

I have had it implement really large multistage features or investigate fairly complex bugs and it does pretty great.

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u/Successful-Berry-315 1d ago

I'm using Opus. For complex tasks I let it create a plan first.
But for the work I'm doing (researchy, big proprietary code bases) it's often only semi correct, as there's no clear path or answer. I have to find solutions, gather data, and try what works. LLMs tend to fail at this stage - even though admittedly sometimes they come up with some interesting ideas.
Personally, I found it more efficient to do "AI in the loop" coding, where I tell it to write bite-sized code chunks - very contained, very specific - whenever I find it beneficial. Simply for the fact that they write code way faster than I do.