Originally published on AI Tech Connect.
What you need to know Prompts-in-code break at team scale. A string pasted into a service has no history, no diff, no rollback target, no eval gate, and locks every edit behind an engineer and a redeploy. The moment a second person needs to iterate, that model fails. Version prompts like the production assets they are. Keep them in git for review and history, and in a registry for environment labels, runtime retrieval by label, and one-click rollback. Decouple the template from the app so a prompt change does not need a deploy. Promote through stages, gate every boundary. Dev to staging to canary to prod, with a golden-set eval and an LLM-as-judge threshold the candidate must pass before it advances. Each boundary is a label move, so rollback is a single pointer change. Close the loop.…
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