Prompt Management Is Infrastructure: Requirements, Tools, and Patterns

Mission Log #6 — Prompt control center: from strings in code to a production-grade system. If your LLM service keeps prompts in code or in a UI without strict version control, you're accumulating technical debt. Not the usual kind. This debt doesn't show up as stack traces. It shows up as silent quality drift: SLAs green, logs clean, and users increasingly getting irrelevant answers. In production, a prompt is the behavioral contract of your service . It directly affects tool-calling accuracy, RAG faithfulness, latency distribution, inference cost, and downstream behavior. This article is not about prompt engineering (how to write a good prompt). It's about prompt management — how to manage prompts as an engineer: version, deploy, roll back, observe, and avoid silent regressions. You'll find: What prompt management is and how it differs from prompt engineering. What production demands from prompt management (and what breaks when you ignore it). A maturity model: where your team is and