this post was submitted on 25 Oct 2025
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"Making games with AI" sounds like hell… like this is what hell must be.
Sitting there prompting AI, getting shitty ass results, prompting it again and again until you eventually settle for slightly less shitty results. The frustration and the loss of agency… oh God, someone should make a psychological horror about this: a frustrated artist forced to ditch their skills and tools and use AI to bring their unique vision to life, and throughout the film you watch them descend deeper and deeper into madness and depression until they burn down a data center and laugh manically as it disintegrates around them.
That's... not what this is about?
The point of integrating AI into games is to provide further diversity within the game.
Think Skyrim. By default you're limited to 3-4 discussion options, right? Imagine now, if you will, that you could just... type in anything, including emotional markers, and have the characters respond interactively to the statement and tone. No longer are you bound by limited dialogue in RPGs.
visual generative AI will just spice up the visuals - hopefully. Things like repetitive textures and such will disappear as the game generates brand new textures for each grid element. Or create tons of background characters without the need to specify them. The list goes on.
Procedural generation with appropriate constraints and a connected game that stores and recalls what's been created can do this far better than a repurposed LLM. It's hard work on the front end but you have a much better idea of what the output will be vs. hoping the LLM "understands" and remembers the context as it goes.
Sorry but procedural generation will never give you the same result as a well tuned small LLM can.
Also there's no "hoping", LLM context preservation and dynamic memory can be easily fine-tuned even on micro models.
I agree that the results will be different, and certainly a very narrowly trained LLM for conversation could have some potentials if it has proper guardrails. So either way there's a lot of prep beforehand to make sure the boundaries are very clear. Which would work better is debatable and depends on the application. I've played around with plenty of fine tuned models, and they will get off track contextually with enough data. LLMs and procedural generation have a lot in common, but the latter is far easier to manage predictable outputs because of how the probability is used to create them.