I’ve seen plenty of people try to make Claude or GPT-4 act like a specialized scheduler. They prompt it heavily: "You are a precise medical assistant. Calculate the next five doses based on X frequency."
The model usually gets it right once. Then it fails when you add constraints—like shifting the entire schedule to land on a Thursday instead of a Tuesday—or when you ask it to validate whether a series of historical dates actually follows the prescribed interval.
LLMs are probabilistic engines. Scheduling is a deterministic problem. When those two worlds collide without a proper interface, you get hallucinations that aren't just annoying; they are dangerous. In healthcare tech, a hallucinated dose date isn't a bug, it's a liability.
Moving logic out of the prompt
If you're building an agentic workflow around patient care or personal health management, you shouldn't be asking the LLM to perform modular arithmetic in its latent space. You should be giving it tools that handle the heavy lifting via structured execution.
