While prompts are easy to copy, the complex engineering work to ensure reliability—validation, versioning, cost controls, and error handling—creates a true competitive moat. This "AI systems engineering" layer is where a product's long-term value and defensibility are built.
For an LLM's output to be useful in a software system, it cannot be treated as ambiguous text. It must be forced through a "hard boundary"—a strict schema or contract—that constrains, validates, and types the data, making it observable and safe for downstream services to trust and consume.