This article argues that using Markdown prompts and LLMs as a runtime interpreter for business rules is slow, expensive, unreliable, and insecure. It shows how mixing regular code, human approvals, and LLMs without a solid framework leads teams to overuse LLMs instead of writing proper code and handling state continuations.

• Using LLMs to interpret Markdown business logic runs ~10,000x slower, costs more, and invites hallucinations, privacy leaks, and prompt-injection attacks than writing regular code.
• The real cause is engineering friction: without a framework to pause/resume code and store continuations, teams dump logic into prompts and let the LLM manage state via conversation history instead of building proper state machines.
• ~90% of support queries follow predictable flows that deterministic code handles better and near-instantly, so LLMs should be reserved for genuinely unstructured tasks like image recognition, sentiment scoring, or free-form-to-query translation.