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Message: <blockquote data-quote="Jacob Lewis" data-source="post: 9696307" data-attributes="member: 6667921">I’ve been experimenting with using LLMs to run TTRPGs for a while now, and one of the most valuable lessons I had to learn—slowly, and sometimes painfully—is how these tools actually function. Not just how to use them, but what they are, and more importantly, what they aren’t.It’s easy to assume you’re working with just another “program.” But that mindset is misleading. LLMs aren’t traditional software executing hard-coded logic or deterministic routines. They're probabilistic language engines that infer structure from input and approximate intent. That difference changes everything.A few things I’ve learned the hard way:<ul> <li data-xf-list-type="ul">They don’t retain context unless you make them. Past sessions, character arcs, lore—it all fades unless you’re actively managing memory, summarizing prior events, or embedding key information into front-load documents every time.</li> <li data-xf-list-type="ul">They’re not GMs. They’re interpreters. They don't "know" the rules, story, or world unless you teach them—consistently, repeatedly, and often redundantly. Expecting consistency without structure is a fast path to disappointment.</li> <li data-xf-list-type="ul">They aren’t aware of their own boundaries. You have to be. They’ll improvise answers confidently, even when wrong. If you don’t define scope, reassert authority, or clarify expectations, the experience can drift fast.</li> <li data-xf-list-type="ul">They’re tools—not magic. Useful, flexible, even impressive in the right hands. But they still operate within hard constraints: token limits, state volatility, and zero actual understanding of the game world beyond what you scaffold into place.</li> <li data-xf-list-type="ul">And yes, they forget. Even if you preload documents or summaries, the model still has to interpret and reconstruct meaning every time. It’s not truly “reading” those docs—it’s absorbing enough to answer the moment. The longer the session or conversation goes, the more memory and processing it needs to manage new input—and the more it has to drop older context to keep functioning. That’s not a bug. That’s just how inference at runtime works.</li> </ul>Once I stopped treating the LLM like a creative partner or “smart assistant” and started treating it like a runtime inference engine—one that requires schema, structure, and clearly bounded roles—it got a lot more usable. And, frankly, more enjoyable.Not perfect. Not autonomous. But incredibly capable within a defined frame.</blockquote>

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