There is a gap between someone who writes good prompts and a team that owns a prompt-driven feature in production. This course is about crossing it. The subject is not phrasing, it is architecture: where prompts live, how they are composed, how they are tested, and how they change without anyone holding their breath.
You begin by pulling prompts out of the codebase into versioned templates with declared variables, then build a small registry that resolves a prompt by name and version at runtime. From there you learn composition, assembling a request from a stable system layer, a task layer, retrieved context and user input, each with its own budget and its own owner.
The middle third covers behaviour under pressure: making instructions survive adversarial user input, keeping a system prompt out of the output, handling conflicting instructions between layers, and designing graceful behaviour when the model cannot comply. You then add a test suite that runs on every change, with fixtures, snapshots for structured output and a small eval set for open text.
The final third is operations. Rolling out a prompt change gradually, comparing versions on live traffic, tracking cost per request, caching what is stable, and keeping an audit trail so that a strange answer in support can be traced to an exact prompt version. You finish with a documented prompt system for a real feature.