Prompt chaining lets a language model take a brief request, expand it into a draft, and then feed that draft into the next prompt for polishing. The shortcut feels like a productivity miracle because each iteration appears to add refinement without extra human effort.

What actually happens is that the model’s own output becomes the only input for subsequent cycles, creating a closed loop that drowns out divergent thinking. In a recent sprint, a product group of about a dozen engineers fed a freshly generated feature outline back into the same model for “risk assessment,” “UX copy,” and “technical spec” prompts.

By the time they presented to leadership, the original spark—a vague customer pain—had been replaced by a tightly worded but narrowly scoped proposal that no longer reflected alternative solutions. The second‑order effect is a hidden erosion of creative variance: the team’s collective intuition, which normally surfaces through spontaneous debate, is supplanted by the model’s internal biases, making future pivots harder and innovation slower.