AI hallucinations are often described as unpredictable failures—or as evidence that generative AI can’t be trusted in regulated environments.  That interpretation is understandable, but incomplete.  In reality, hallucinations occur because large language models generate text based on probability, not verification. They are a predictable result of how AI systems express confidence when certainty is unavailable. Once that’s understood, hallucinations become easier to anticipate

One of the most common frustrations teams encounter when using AI for medical writing is the feeling that they’re constantly re-explaining their standards. The instinctive response is to write longer prompts. More detailed prompts. Carefully engineered prompts. But prompting isn’t memory—and it isn’t training. Understanding the difference between prompting and fine-tuning is critical if AI is going to become reliable rather than exhausting. Prompting Defines the Task, Not the

If you’ve ever watched an AI tool do a solid job drafting a section—only to cut off a table, ignore an earlier definition, or unravel at the end—you’ve probably assumed the issue was the prompt.  Often, it isn’t.  In many cases, the underlying issue is tokenization, a foundational machine learning concept that directly affects how generative AI processes medical and regulatory documents. Tokenization determines how text is broken down, how much context an AI model can retain,