By late June 2026, the silent failure mode in many teams was prompt sprawl: everyone kept a private chat history and there was no shared baseline. When a model changed or a colleague left, quality dropped quietly.

Prompt operations means treating each instruction like a small product: a named owner, versioning, sample inputs/outputs, a review date, and a retirement date. Without that, the “best prompt on the team” stays buried in someone’s private chat.

Start with a shared library in the team workspace and import only high-frequency prompts — support, summarization, finance drafts. Retest those fixed samples after every model change so quality regressions are visible.

The goal is not bureaucracy; it is predictable output after staff turnover or a model upgrade.

Manager action: Create a shared prompt library with owner, last review date, and sample inputs/outputs.