> A simple example might be the problem of "pick a color".
People still underestimate the power of LLMs. You ask it to show you a color picker, it generates HTML code for a color picker, you copy that into your browser and you can pick your color, which you can then copy&paste back into the LLM for further processing.
This already works and no human had to code a color picker into ChatGPT for this (and this is why LLMs are scary).
More broadly speaking I find the idea of "LLM apps" a bit problematic, it's basically the modern Microsoft Bob. The LLM itself is already the most powerful app you can think of. Trying to hide it with a UI that looks a little more than what you are already familiar with is removing its expressive power.
>> There are no better examples than Stable Diffusion prompts.
StableDiffusion prompts are a terrible example for the power of modern LLMs, as StableDiffusion has extremely primitive understanding of language, unlike ChatGPT. With StableDiffusion you are really just laying keywords and concepts together hoping that something interesting will happen. The moment you ask it for anything even remotely complex it falls apart. Ask it to generate "blue hair" and it might give you blue hair, but it will also paint random other objects in the image blue. Even simple attributes don't stick to the objects you assigned them to. Complex actions or expressions don't work at all. You have to use ControlNet, in-painting and other tricks to create complex images. The language model of StableDiffusion just can't handle it and the image generation itself is also lacking in generalization (i.e. you need custom trained models for specific styles or topics). It also doesn't allow the iterative refinement that you can do in ChatGPT, you only get a single prompt.
Prompt engineering is a short term workaround for the limitations of the current models. But that is going away. After all you have a LLM at your finger tips and guess what that's good for: generating text, which includes prompts.