I strongly recommend giving Gemini 2.5 Pro a shot. Personally I don't like their bloated UI, but you can set the temperature value, which is especially helpful when you are more certain what and how you want, then just lower that value. If you want to get some wilder ideas, turn it up. Also highly recommend reading the thought process it does! That was actually key in having very complex ideas working. Just spotting couple of lines there, that seem too vague or even just a little bit inaccurate ... then pasting them back, with your own comments, have helped me a ton.

Is there a specific part in which you struggle? And FWIW, I've been on a heavy learning spree for 2 weeks. I feel like I'm starting to see glimbses from the barrel's bottom ... it's not so deep, you just gotta hang in there and bombard different LLMs with different questions, different angles, stripping away most and trying the simplest variation, for both prompt and godot. Or sometimes by asking more general advice "what is current godot best practice in doing x".

And YouTube has also been helpful source, by listening how more experienced users make their stuff. You can mostly skim through the videos with doublespeed and just focus on how they are doing the basics. Best of luck!

I'm talking more broadly, as well, including consideration of audio, video, and image modalities, general robotics models, and the momentum behind applying some of these architectures to novel domains. Protocols like MCP and automation tooling are rapidly improving, with media production and IT work rapidly being automated wherever possible. When you throw in the chemistry and materials science advances, protein modeling, etc - we have enormously powerful AI with insufficient compute and expertise to apply it to everything we might want to. We have research being done on alternate architectures, and optimization being done on transformers that are rapidly reducing the cost/performance ratio. There are models that you can run on phones that would have been considered AGI 10 years ago, and there doesn't seem to be any fundamental principle decreasing the rate of improvement yet. If alternate architectures like RWKV get funded, there might be several orders of magnitude improvement with relatively little disruption to production model behaviors, but other architectures like text diffusion could obsolete a lot of the ecosystem being built up around LLMs right now.

There are a million little considerations pumping transformer LLMs right now because they work and there's every reason to expect them to continue improving in performance and value for at least a decade. There aren't enough researchers and there's not enough compute to saturate the industry.

In practice you have to evaluate the models yourself for any non-trivial task.