Prompts as Code, Not Chat
Explore how to write task-shaped prompts treated as code rather than casual chat messages. Learn to define tasks, constrain outputs, specify formats, and handle ambiguous inputs to improve reliability in AI-powered backend integrations.
It’s easy to write a prompt as an informal natural language request: provide a rough, conversational description of the task and hope the resulting output is usable. That approach is fine for exploring an idea. For prompts used in backend services, however, it is often too underspecified. Unspecified details must be resolved during generation and can introduce behavior that the application did not define explicitly. A structured prompt defines those constraints up front, much like defining a function’s inputs, outputs, constraints, and expected behavior before implementation.
A prompt is a spec, not a message
A task-shaped prompt for a backend job typically needs four things a casual chat message usually skips: a clear statement of the task, the exact set of allowed outputs, an explicit output format, and instructions for what to do when the input doesn't cleanly fit. Let’s build one as a function, since treating prompt construction as code, with parameters, not hardcoded strings, is exactly the mindset this lesson is named after.
Lines 1–2: We define
build_ticket_prompt, which accepts the permitted categories and ticket text. We then join the categories into one comma-separated string. ...